Abstract

We compared deep learning models as a basis for OCT image-based feedback system for smart laser osteotomy. A total of 10,000 OCT image patches were acquired ex-vivo from pig’s bone, bone marrow, fat, muscle, and skin tissues. We trained neural network models using three different input features (the texture, intensity profile, and attenuation map). The comparison shows that the DenseNet161 model with combined input has the highest average accuracy of 94.85% and F1-score of 94.67%. Furthermore, the results show that our method improved the accuracy of the models and the feasibility of identifying tissue types from OCT images.

© 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. Introduction

Bone surgery (osteotomy) has been practiced for treatment of various bone disorders in centuries. The instruments used in such surgeries have not changed much over time, such as saws, drills, chisels, and hammers). The primary mechanism generally is by applying mechanical stress to the bone surface until the instrument break the bone, producing high pressure, friction, and vibration [1]. As a result, there is a tendency to remove more tissue than necessary [2,3]. Additionally, the contact between tissue and the tool’s surface increases the risk of bacterial contamination [4].

On the other hand, laser bone surgery (laser osteotomy) is an emerging technique that promises to overcome the limitations of using conventional mechanical tools. Laser osteotomy is a contactless intervention tool that is capable of delivering a precise cut, which, in turn, reduces tissue loss during the ablation procedure, and supports faster tissue healing [4]. The non-contact application of the laser osteotomy reduces the potential risk of bacterial contamination [5]. Moreover, the photothermal effect after absorption of a laser (such as Er:YAG laser) beam causes micro explosions and breakup of bacteria. This effect subsequently leads to bacterial death in the ablation spot [68].

Furthermore, by embedding the laser in a robotic arm, small complex shapes, such as a dovetail, diamond, or circle, are made possible. [2]. However, similar to mechanical tools, laser osteotomy may also cause collateral damage if critical tissues such as nerve and bone marrow are not avoided. Thus, a feedback mechanism capable of distinguishing tissue types during ablation becomes a critical focus for research. Several studies have explored tissue type identification based on physical feedback properties. Non-invasive methods such as Raman [911], autofluorescence [1216], and diffuse reflectance spectroscopy [1719] have been demonstrated to have a high sensitivity to differentiate tissue type. Nevertheless, these techniques work in point measurement, thus, losing the tissue’s margin or morphological information. Other methods such as acoustic shock waves and laser-induced breakdown spectroscopy also have the potential to differentiate tissue types [2025]. However, these methods distinguish tissue type only after an ablation pulse has been applied, thus, increase the risk of collateral damage during laser ablation.

Our project proposed a non-destructive tissue identification system based on Optical Coherence Tomography (OCT) imaging. OCT is the most applicable tool for margin detection and also provides morphological information [26]. OCT offers an alternative approach to real-time tissue classification, It is rapidly becoming the preferred method for real-time in-vivo investigation of thin tissues and subsurface imaging because of its high resolution and non-invasive nature [2628]. Several studies combining OCT and laser ablation treatment have reported experiments demonstrating OCT-guided laser surgical concepts for clinical application. The combinations reportedly increased resection accuracy and precision for brain tumors and blood coagulation [2931]. This imaging system implemented an intra-operative diagnosis by providing positional feedback and tissue specificity during surgery. In our approach, we extend the application of the OCT-guided laser surgical concepts for smart laser osteotomy. In this manner, we focused more on discriminating between the bone tissue and the surrounding tissues. We wanted to avoid damaging the nerve and bone marrow (inside the bone), which may lead to complications if it is accidentally cut. Other than that, the feedback system will help to prevent any damage to surrounding tissue (such as muscle and skin) due to accidental patient movement.

Tissue characterization or classification has most recently been the focus of research in the medical application of OCT. Several machine learning approaches, such as random forest [3234] and support vector machine (SVM) [35,36], have been demonstrated to achieved average accuracy of 80.37% and 96.80%, respectively, for tissue characterization of atherosclerotic plaques (fibrous, calcific, and lipid-rich). These methods involved the attenuation coefficient, statistical and geometrical features of the image. Although, these methods have shown good accuracy for automatic tissue classification. The complicated feature extraction of the image increased the prediction time, which is a critical point for real-time laser ablation monitoring.

As a subset of machine learning, deep learning is developing linearly to the advances of computation technology in recent years. The introduction of the convolutional neural network (CNN) bring a significant breakthrough for automatic medical image analysis and recognition. CNN is often used to classify, retrieve, correct, and segment medical images. In the field of OCT image classification, CNN has been demonstrated to achieve similar or even better classification accuracy and sensitivity with the classical machine learning methods. Previously, Abdolmanafi et al. used the AlexNet and VGG19 models for the intima and media tissue classification [37]. These models were investigated to have an average accuracy of 96.0% and 98.0%, respectively. Furthermore, Gessert et al. reported that the ResNet50V2 and DenseNet121 models have an average accuracy of 91.3% and 91.0%, respectively, to classify the plaque area as lipid tissue, fibrous tissue, and calcified tissue [38]. Although deep learning model required an extensive amount of training data and more extended training time. Nevertheless, the deep learning model performs faster image classification by skipping the feature extraction process, consequently enabling the implementation of real-time tissue classification.

In this paper, we compared well-established CNN models to classify several healthy tissue types, the first application of its kind to the best of our knowledge. We focused on distinguishing the tissue types most likely to be encountered during laser osteotomy, such as bone, bone marrow, fat, muscle, and skin. As a preliminary experiment, we investigated the CNN’s ability to classify tissues in the absence of the ablation laser. In the future, we foresee integrating the CNN model for real-time tissue differentiation during laser osteotomy.

2. Materials and methods

The ultimate aim of the smart laser osteotomy is to provide feedback on the tissue types which would be encountered during laser ablation to avoid cutting critical tissues. Here, we use an OCT imaging system to monitor tissue anatomy at the subsurface level during laser ablation. A patch image taken from the ablation area is used as input of the CNN model to discriminate between tissue types. The output of the CNN model provides feedback for the ablation laser to either stop or continue ablating. Our smart laser osteotomy concept is illustrated in Fig. 1. This paper aimed to find the CNN model with high accuracy in distinguishing tissue type and short prediction time. The output of the CNN will be used as feedback to close or open an optical shutter for controlling the ablation laser (e.g Er:YAG or Nd:YAG Laser). However, as a preliminary experiment, we investigated the CNN’s ability to classify tissue in the absence of the ablation laser.

 figure: Fig. 1.

Fig. 1. Schematic of the proposed OCT-based smart laser surgery system. We used a Fourier-Domain OCT with an Axsun swept-source laser. The OCT laser (red line) is coupled with an ablation laser (blue line) by a dichroic filter. The OCT images are streamed to monitor the ablation process. A region of interest (image patch) from the OCT image is selected on the ablation spot. We trained a convolutional neural network model to identify tissue type based on the extracted image patch. The convolutional neural network’s output provides feedback to an optical shutter and controls the ablation laser to either stop or continue ablation.

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2.1 OCT imaging system

OCT image acquisition is the starting point of the proposed smart laser surgery system. We used a custom OCT system equipped with an Axsun swept-source laser. The laser source operated at a central wavelength ($\lambda _c$) of 1046 nm, a spectral bandwidth of 112.15 nm, and an A-scan line rate of 100.16 kHz. The imaging range of the OCT system was equal to 3.6 mm in the air. The OCT system provided a field of view of 4.8 $\times$ 4.8 mm$^{2}$. The image size acquired with this OCT system was 1024 pixels in height and 300 pixels in width. The sensitivity ($SNR_{max}$) of our OCT system was 96.46 dB. The corresponding lateral and axial resolutions were 44$\mathrm{\mu}$m and 10$\mathrm{\mu}$m, respectively.

During acquisition, frame-averaging method was used to acquire high-quality images by averaging several image frames from a single sample location on the tissue. This method reduced the noise originating from random interference signals and natural bandpass filter problems while still preserving the speckle signal as the primary information carrier [39]. We ensured that the tissues were statically placed during image acquisition to avoid motion artifacts (a potential disadvantage of the averaging method).

2.2 Image patch extraction

The next step of our smart laser osteotomy was image patch extraction from OCT image. The patch was selected in such a way to represent a region of interest where a destructive laser pulse would be applied and used as input to the CNN to identify tissue types. We defined the ablation spot as always in the lateral center of the image. Vertical Canny edge detection method was used to trace the tissue surface in axial direction because of its simplicity and low sensitivity to noise [40,41]. After getting the ablation spot’s lateral and axial location, a square 128 x 128 pixels image patch was extracted with the ablation spot as the top center of the image patch. We defined the image patch as the texture feature and used it as the input of the CNN model.

2.3 Neural network frameworks

This study aimed to find a CNN model capable of accurately and efficiently classifying tissue type based on the extracted image patch. We evaluated well-established CNN models to find the best model with the highest accuracy and fastest processing speed. In this study, we use four main primary CNN models and define them as the base models. The first base model was the AlexNet, developed by Alex et al. [42]. The second base model was the deep Visual Geometry Group model (VGG), used by Simonyan & Zisserman for image-based object recognition [43]. The third base model was the deep Residual Network model (ResNet), shown by He et al. [44]. The last base model was the deep Densely Connected Network model (DenseNet), proposed by Huang et al. [45].

We also exploited the variants for each CNN base model except for the AlexNet model. We evaluate the VGG based model with the variance of 11, 13, 16, and 19 -layers (VGG11, VGG13, VGG16, and VGG19). Additionally, we evaluated the VGG model with shallower depth by removing the last two convolutional blocks (we defined it as VGG-3Block). The VGG-3Block framework was similar to VGG13, but we used only the first three convolutional blocks and directly connected the convolutional layer’s output to the fully connected layer. Additionally, we also evaluated the effect of removing the fully connected layer (defined as VGG16-A) or adding two more fully connected layers (defined as VGG16-B) at the end of the VGG16. Furthermore, we evaluated the ResNetV2 model with the variance of 50, 101, and 152 residual layers depth (ResNet50V2, ResNet101V2, and ResNet152V2). Finally, we evaluated the DenseNet model with variance of 121, 161, 169, and 201 dense layers (DenseNet121, DenseNet161, DenseNet169, and DenseNet201).

2.4 Feature fusion

Intuitively, we could train the CNN models directly to classify tissue type based on the texture feature alone. However, previous studies suggest additional features to support the texture feature could improve the tissue classification accuracy. Rico-Jimenez et al. proposed using a single A-line (defined as intensity profile feature) to characterize atherosclerotic plaques for a faster prediction process and reported to achieve an average classification accuracy of 85% [46]. In this study, we believed that combining the texture and the profile features will improve the accuracy of the CNN models. Therefore, our first approach was combining the texture feature with the intensity profile feature which was extracted from the middle of the image patch. The extraction and combination process for the first approach is illustrated in Fig. 2. In the beginning, we independently trained two CNN models. We trained the first model (CNN Texture model) to classify tissue type by using the 2-dimensional texture feature as input. Concurrently, we trained the second model (CNN Profile model) by using a 1-dimensional intensity profile as input. Both models were constructed using the same base model architecture. However, for the second model, we changed the input size of the network to accept 1-dimensional input and used the 1-dimensional convolutional layers instead of 2-dimensional layers. After training both CNN models, we fused them to concatenate each output to an intermediate layer. The intermediate layer was a fully-connected layer with 4096 neurons. Finally, we retrained the combination to fine-tune the intermediate layer. We defined this combination as combination-A.

 figure: Fig. 2.

Fig. 2. Combination-A. The CNN texture and CNN profile models were trained separately. The first model was trained to identify tissue type based on the texture feature. The second CNN model was trained to identify tissue type based on the intensity profile feature in the middle of the image patch. Both of the CNN model’s outputs were then fused with a concatenation layer. The fused feature was connected to a fully connected layer of 4096. At the end layer, a sigmoid activation function was used to identify the tissue type.

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Furthermore, Ughi et al. combined the texture and attenuation coefficient features for atherosclerotic plaque tissue characterization [32]. We also believed that the combination would improve the accuracy of the CNN models. Therefore, in our second approach, we combined the texture feature with a pixel-wise attenuation coefficient map. The extraction of the depth-resolved attenuation coefficient map was proposed by Vermeer et al. to have a pixel-wise attenuation coefficient map from the image [47]. We reconstructed the attenuation map before extracting the image patch. Then, we extracted the attenuation coefficient map patch at the same location as the texture image patch. These two patches were then fused into a 2-channel image and used as the CNN model’s input. Figure 3 illustrates the processing of the second approach. We defined this combination as combination-B.

 figure: Fig. 3.

Fig. 3. Combination-B. The CNN model was trained to differentiate tissue type based on 2-channel image input. This 2-channel image was constructed by combining the image patch and the attenuation patch. The attenuation patch was extracted from the attenuation map at same location with image patch.

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2.5 Attenuation coefficient map reconstruction

The attenuation coefficient maps were reconstructed using the model of light transmission which has been introduced in the work of Vermeer et al. [47]. The light transmission model of an A-Scan is defined with

$$I(z) \cong \beta \ L_0 \ \mu_b(z) \ e^{{-}2\int_{0}^{z}\mu_a(u)du},$$
where $I(z)$ is the intensity signal detected at depth $z$, $\beta$ defines the analog to digital conversion factor of the digitizer and the detector’s quantum efficiency, $L_0$ is the source light power ($W m^{-2}$), $\mu _a$ denote the attenuation coefficient, and $\mu _b$ denote the backscatter coefficient. We assume that the backscatter coefficient is a fixed fraction $\alpha$ of the attenuation coefficient ($\mu _b = \alpha \mu _a$) and the $I(\infty ) = 0$. The integral of $I(z)$ from $z$ to infinity ($\infty$) is then given by
$$\begin{aligned} \int_{z}^{\infty}I(u)du & \cong{-}\frac{ \alpha \beta L_0}{2} e^{{-}2 \int_{0}^{u} \mu (v) dv } + C \Bigg\vert_z^\infty\\& ={-} \frac{I(u)}{2 \mu_a(u)}+ C \Bigg\vert_z^\infty\\& ={-} \frac{I(z)}{2 \mu_a(z)}. \end{aligned}$$

We can rewrite Eq. (2) to get the estimated attenuation coefficient as

$$\mu_a \cong \frac{I(z)}{2 \int_z^\infty I(u) du} .$$

The pixel-wise form of the estimated attenuation coefficient is given by

$$\mu_a[ i,j ] \approx \frac{I[i,j]}{2 \delta \sum_{z=j+1}^\infty I[i,z]} ,$$
where $\delta$ denotes the axial pixel spacing (mm/pixel), $i$ denotes the A-Scan index (horizontal index) over the image, and $j$ denotes the index over the depth of $i$-th A-Scan (vertical index). Furthermore, the image $I[i,j]$ is first denoised with Gaussian filter to reduce the speckle noise.

2.6 Image data collection

The tissue samples were taken ex-vivo from five individual pigs. For each pig, we took tissue samples from the femur bone. Figure 4 illustrates the femur bone anatomical structure of a pig. The OCT images were acquired from five different tissue types (bone, bone marrow, fat, muscle, and skin). We prepared 400 tissue samples for each tissue type from random locations around the diaphysis and epiphysis areas. Figure 5 shows examples of the tissue sample. A B-Scan image was acquired for each tissue sample. All of the B-Scan images were enhanced using frame-averaging over 300 frames per image. Therefore, 2000 OCT images were acquired for each pig. In total, we acquired 10000 OCT scan images. Furthermore, the classification was done by using the image patch as the input. We extracted an image patch for each OCT image. The image patch extraction is explained in section 2.2.

 figure: Fig. 4.

Fig. 4. Illustration of the femur bone anatomical structure of a pig (left). Cross-cut example of the bone on the diaphysis area is illustrated on the right image.

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 figure: Fig. 5.

Fig. 5. The left column shows examples of the bone, bone marrow, fat, muscle, and skin tissue samples used in our experiment. The corresponding OCT images (middle column) were scanned on the red line for each tissue sample. The last column (right) shows the attenuation maps reconstructed from the OCT images. The image patches (red box) were taken on the surface of the tissue and used to train the CNN models.

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In the experiment, we used leave-one-out of five pigs (5-fold) cross-validation to measure the performance of the CNN models. In one fold, we trained the CNN model with patch images from four animals (8000 image patches) and tested it with one other animal (2000 image patches).

2.7 Models training

We implemented the CNN models in Keras python implementation, with a TensorFlow backend [48]. The training workstation was equipped with an Intel Xeon E5620 processor and two NVIDIA GTX 1080 Ti GPUs. All models were trained with 1000 epochs and a batch size of 32 samples to fit the GPU’s memory capacity. We defined cross-categorial entropy as the training loss function and Adam (learning rate = 1.0 x $10^{-4}$) as the training optimizer. We evaluated the classification performance of the CNN models in terms of average cross-validated accuracy and F1-score. The F1-score is a measure of the model’s average accuracy for each class (tissue type). The F1-score of a class can be calculated from

$$\% F_1-score = \frac{TP}{TP + \frac{1}{2}(FP + FN)} \times 100\,\%,$$
where TP is the number of correctly labelled samples in current class, FP is the number of incorrectly labelled samples as belonging to the current class, and, FN is the number of incorrectly labelled samples as belonging to other class [49].

3. Results and discussion

3.1 Classification performance

Our first experiment was to find the optimal CNN models (VGG, ResNet, and DenseNet) by using different number of layers except for the AlexNet model because the VGG model has almost the same configuration as the AlexNet with more convolutional layers and smaller filter sizes. Here the CNN models were trained with the texture feature only. Specifically for the VGG based models, the results show that the top fully connected layers play an important part in classification accuracy. The VGG model has less accuracy and F1-score when we removed the fully connected layer (VGG16-A). However, we observed similar classification performance when we use more fully connected layer (VGG16 and VGG16-B). Other than that, the number of convolutional layer blocks also plays an important part in the VGG model’s performance. The VGG-3block model has even less accuracy and F1-score in comparison with the AlexNet. The VGG19 model has the best performance among the VGG models. Furthermore, the results for the ResNet models show that the accuracy and F1-score are higher when we used more number of layers. Finally, we observed that the DenseNet121 model performed best than the other models. However, we also observed a relatively small difference in accuracy and F1-score between the DenseNet models. It is difficult to conclude the relation between the number of layers and the performance improvement of the DenseNet models. Table 1 and 2 further show the accuracy and F1-score of the AlexNet and DenseNet models. The accuracy and F1-score for the other models can be found in Appendix A (Table 5 and 6).

Tables Icon

Table 1. Comparison of the average $\pm$ standard deviation of accuracy for the AlexNet and DenseNet models trained with the texture, profile, attenuation, and combinations of features (Combination A and B). The highest average accuracies are highlighted in bold.

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Table 2. Comparison of the average $\pm$ standard deviation of F1-score for the AlexNet and DenseNet models trained with the texture, profile, attenuation, and combinations of features (Combination A and B). The highest average F1-scores are highlighted in bold.

In the second experiment, we also discussed the classification performance of the CNN models with three different input features (texture, intensity profile, and attenuation map feature). Examples of the attenuation maps are shown in Fig. 5. The results show that all the CNN models achieved higher accuracy by learning the texture feature than learning the profile or attenuation features. We also observed that the CNN models trained with the attenuation feature have higher accuracy and F1-score than those trained with the profile feature. Although we may discriminate tissue type based on the average attenuation coefficient, the standard deviations indicate noises that reduce the classification performance (see Table 3). Furthermore, the results prove the improvement of accuracy by combining the texture and profile or attenuation features. We observed that combination-B improves the models’ performance better than combination-A. The combination-B increases the accuracy and F1-score of all texture feature-based models by an average of 3.59 % and 3.60 %, respectively. On the other hand, the combination-A increases the accuracy and F1-score of all texture feature-based models by an average of 3.00 % and 2.98 %, respectively, which proves that the attenuation map feature discriminates better than the profile feature. These accuracy improvements happened because the CNN models learn the discriminant between bone marrow and fat better when trained with the profile or attenuation features than the texture feature. As an example, Fig. 6 illustrates the benefit of using the combinations to improve the DenseNet121 models.

 figure: Fig. 6.

Fig. 6. The test confusion matrix of the DenseNet121 models that were trained with the texture feature (a), the profile feature (b), and the attenuation of both features (c). The model trained with texture feature have lower accuracy in classifying bone marrow and fat. On the other hand, the profile and attenuation feature discriminate better for the bone marrow and fat. Therefore, the model has higher accuracy in the combination A and B (d) and (e).

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Table 3. The average $\pm$ standard deviation of the tissues’ attenuation coefficient. The average attenuation coefficient was measured based on the reconstructed attenuation coefficient map patches

3.2 Computation Performance

The computation performances of the models were evaluated for the average prediction time to classify the image patches. We tested the computational performance of the models on the same workstation that we used to train the models. The prediction time measurements were the average prediction time of five prediction runs for each model. With the texture feature alone as the input, both the AlexNet and VGG based models predicted the tissue type in less than 40 msec. We observed that the computation time of the VGG based models does not significantly increase along with the increased number of layers. Furthermore, the ResNet and DenseNet models predicted the tissue type in around 40~70 msec. Here, the ResNet and DenseNet models’ prediction time increased along with the increasing number of layers. The AlexNet model predicted the tissue type faster than the models because it used fewer layers and less network complexity (parameters).

We also measured the prediction time when using the profile and attenuation features as the models’ input. The models predicted the tissue type faster when we used the profile feature than the texture and attenuation features as input because of the smaller number of parameters. Additional delay for attenuation map extraction also increased the prediction time when we used the attenuation feature as the input. The attenuation map extraction delayed the prediction time by 18.76 $\pm$ 1.36 msec. On the other hand, there is no specific processing time for extracting the intensity profile. Furthermore, the previous section demonstrated that combining the texture feature with the profile or attenuation features significantly increased the classification accuracy and sensitivity. In principle, the prediction time for both combination input models (combination A and B) were similar to those trained with the texture feature alone. However, in combination-B, the extraction of the attenuation map put an additional delay in the prediction time. Table 4 show the computation performance of the AlexNet and DenseNet models. The computation performance for the other models can be found in Appendix A (Table 7).

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Table 4. Comparison of the average $\pm$ standard deviation of the prediction time for the AlexNet and DenseNet models trained with the proposed features. The prediction time includes the attenuation coefficient map extraction time for the combination-B model and the model with attenuation maps input. The attenuation coefficient map extraction takes 18.76 $\pm$ 1.36 msec. The fastest computation performances are highlighted in bold.

All models’ computation performances were below 90 msec, excluding the image acquisition and frame-averaging. Our results are faster than the reference machine learning methods [3236]. These results show the possibility of achieving real-time tissue classification with the optimum pulse repetition rate of our laser ablation was 10 Hz (100 msec per pulse) [27]. Nevertheless, the results show that the AlexNet model predicted the tissue type fastest than the other models even when using the combined features as the input. However, we prioritize more on the classification accuracy in this study. Therefore, within the pulse repetition rate window, the DenseNet161 with the texture and attenuation feature combination input would be the best option to predict the tissue type.

4. Conclusions and outlook

We demonstrated that the classification accuracy was significantly increased by combining the texture feature with the intensity profile or attenuation map features. Combining the texture feature with the attenuation map improved the classification accuracy by an average of 3.59%. Meanwhile, the combination with the profile features improved the classification accuracy by an average of 3.00%. The difference in accuracy’s improvement between both combinations was relatively small, especially between the DenseNet models. However, the attenuation feature extraction delays the prediction time by 18.76 msec. Therefore, in practice, the combination between the texture and intensity profile is preferable to the combination between the texture and attenuation map.

The results for the combination between the texture and intensity profile show that DenseNet models have higher accuracy than the other models. Specifically, the DenseNet161 model has the highest accuracy compared with the other models. However, the complexity of the DenseNet161 model also increased the prediction time. Although the AlexNet model has 1.46% less accuracy than the DenseNet161, this model’s prediction time is ~1.43 times faster. If low computation resource is available, such as in an embedded system, the AlexNet model would be a better choice than the DenseNet161 model. For our smart laser surgery, the computation time required by all models is still carried below the optimum ablation laser pulse rate, which suggests the application for real-time feedback. This study suggests that the DenseNet161 with the texture and attenuation feature combination input would be the best option to predict the tissue type.

The frame-averaging image enhancement will delay the feedback system by ~900 msec for acquiring 300 frames (B-Scan) per image in this study. We would consider using a smaller number of frames for a faster feedback system in the real-time implementation. However, the reconstructed images will have lower image quality compared to the images used in this study. Consequently, the CNN may have lower accuracy if trained using the images with lower image quality. Several studies suggested faster OCT image denoising, such as block-matching 3D (BM3D) [50], double-density dual-tree complex wavelet transform (DD-CDWT) [51], or deep learning denoising [52] methods, as an alternative to the frame-averaging method. These methods are demonstrated to be able to reconstruct a high-quality image from a single raw image. In such a way, we could directly train the CNN using the denoised raw images to identify tissue type. However, there is a possibility that the denoising method will alter the important information (such as textural and attenuation features) for tissue classification. Further investigation is needed to ensure that the CNN performs similarly with this study.

Furthermore, we are aware that implementing this method and incorporating it into a laser ablation system remains challenging. One of the challenges is that the tissue will experience an increase in temperature (heating up) during microsecond ablation. The area of interest for the tissue classification would experience heat transfer from the ablated tissue in the focal spot area. This process happens in a fraction of a second before the ablated tissue in the focal spot area explodes or evaporates and is confined to the laser’s short pulse duration. Therefore, this indirect heating process can be considered slow heating.

Additionally, the optical properties of the tissue (such as refractive index, absorption coefficient, and scattering coefficient) would change due to the heating process [18]. These changes will affect the tissue classifier’s (CNN) accuracy. Further studies are needed to determine how the CNN performs during laser ablation. We planned to train and test the performance of the CNN models with two approach conditions. In the first approach, we will train and test the CNN modes using the images that are collected in controlled temperature conditions. For example, we would collect the OCT images while heating the tissue in every 5 °C increment up to 100 °C. The second approach is by directly collecting OCT images during laser ablation and use them for training and test the CNN models.

Finally, for in-vivo experiments, we will also face challenges such as bleeding and tissue debris from the ablated tissue, which might induce artifacts to the OCT image. Therefore, we will also investigate the feasibility of integrating a cooling system such as pressurized air and/or water irrigation with laser ablation in our future work. The cooling system will be helpful to maintain the tissue temperature and cleaned the ablation area from bleeding and tissue debris.

Appendix A: Tables on accuracy, F1-score, and computation time for all models

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Table 5. The average $\pm$ standard deviation of accuracy for all models. The highest average accuracies are highlighted in bold.

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Table 6. The average $\pm$ standard deviation of F1-score for all models. The highest average F1-scores are highlighted in bold.

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Table 7. Comparison of the average $\pm$ standard deviation of the prediction time for all models. The prediction time includes the attenuation coefficient map extraction time for the combination-B model and the model with attenuation maps input. The attenuation coefficient map extraction takes 18.76 $\pm$ 1.36 msec. The fastest computation performances are highlighted in bold.

Funding

Werner Siemens Foundation through the Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE (MIRACLE) project.

Acknowledgment

The authors acknowledge Iris Schmidt and Yifan Jian for the modification of our custom-made OCT system.

Disclosures

The authors declare no conflicts of interest.

Data availability

Data underlying the results presented in this paper are not publicly available at this time for further continuation of the study. However, the data may be obtained from the authors upon reasonable request.

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10. E. Cordero, I. Latka, C. Matthäus, I. Schie, and J. Popp, “In-vivo raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018). [CrossRef]  

11. P. C. Ashok, M. E. Giardini, K. Dholakia, and W. Sibbett, “A raman spectroscopy bio-sensor for tissue discrimination in surgical robotics,” J. Biophotonics 7(1-2), 103–109 (2014). [CrossRef]  

12. F. Stelzle, M. Rohde, M. Riemann, N. Oetter, W. Adler, K. Tangermann-Gerk, M. Schmidt, and C. Knipfer, “Autofluorescence spectroscopy for nerve-sparing laser surgery of the head and neck–the influence of laser-tissue interaction,” Lasers Med. Sci. 32(6), 1289–1300 (2017). [CrossRef]  

13. F. Stelzle, C. Knipfer, W. Adler, M. Rohde, N. Oetter, E. Nkenke, M. Schmidt, and K. Tangermann-Gerk, “Tissue discrimination by uncorrected autofluorescence spectra: A proof-of-principle study for tissue-specific laser surgery,” Sensors 13(10), 13717–13731 (2013). [CrossRef]  

14. A. Zam, J. Franke, and M. Merklein, Optical Tissue Differentiation for Sensor-controlled Tissue-specific Laser Surgery (Meisenbach, 2011).

15. R. Gunaratne, J. Goncalves, I. Monteath, R. Sheh, M. Kapfer, R. Chipper, B. Robertson, R. Khan, D. Fick, and C. N. Ironside, “Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (drs),” Biomed. Opt. Express 11(9), 5122–5131 (2020). [CrossRef]  

16. F. Fanjul-Vélez, S. Pampín-Suárez, and J. L. Arce-Diego, “Application of classification algorithms to diffuse reflectance spectroscopy measurements for ex vivo characterization of biological tissues,” Entropy 22(7), 736 (2020). [CrossRef]  

17. R. Gunaratne, I. Monteath, J. Goncalves, R. Sheh, C. N. Ironside, M. Kapfer, R. Chipper, B. Robertson, R. Khan, and D. Fick, “Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data,” Biomed. Opt. Express 10(8), 3889–3898 (2019). [CrossRef]  

18. A. Zam, F. Stelzle, K. Tangermann-Gerk, W. Adler, E. Nkenke, F. W. Neukam, M. Schmidt, and A. Douplik, “In vivo soft tissue differentiation by diffuse reflectance spectroscopy: preliminary results,” Phys. Procedia 5, 655–658 (2010). [CrossRef]  

19. F. Stelzle, K. Tangermann-Gerk, W. Adler, A. Zam, M. Schmidt, A. Douplik, and E. Nkenke, “Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery,” Lasers Surg. Med. 42(4), 319–325 (2010). [CrossRef]  

20. H. Nguendon Kenhagho, G. Rauter, R. Guzman, P. C. Cattin, and A. Zam, “Optoacoustic tissue differentiation using a mach-zehnder interferometer,” IEEE Transactions on Ultrason. Ferroelectr. Freq. Control. 66(9), 1435–1443 (2019). [CrossRef]  

21. E. Bay, A. Douplik, and D. Razansky, “Optoacoustic monitoring of cutting efficiency and thermal damage during laser ablation,” Lasers Med. Sci. 29(3), 1029–1035 (2014). [CrossRef]  

22. V. Periyasamy, C. Özsoy, M. Reiss, X. L. Deán-Ben, and D. Razansky, “In vivo optoacoustic monitoring of percutaneous laser ablation of tumors in a murine breast cancer model,” Opt. Lett. 45(7), 2006–2009 (2020). [CrossRef]  

23. H. Abbasi, L. M. B. Bernal, A. Hamidi, A. Droneau, F. Canbaz, R. Guzman, S. L. Jacques, P. C. Cattin, and A. Zam, “Combined nd:yag and er:yag lasers for real-time closed-loop tissue-specific laser osteotomy,” Biomed. Opt. Express 11(4), 1790–1807 (2020). [CrossRef]  

24. R. Kanawade, F. Mahari, F. Klämpfl, M. Rohde, C. Knipfer, K. Tangermann-Gerk, W. Adler, M. Schmidt, and F. Stelzle, “Qualitative tissue differentiation by analysing the intensity ratios of atomic emission lines using laser induced breakdown spectroscopy (libs): prospects for a feedback mechanism for surgical laser systems,” J. Biophotonics 8(1-2), 153–161 (2015). [CrossRef]  

25. F. Mehari, M. Rohde, C. Knipfer, R. Kanawade, F. Klämpfl, W. Adler, N. Oetter, F. Stelzle, and M. Schmidt, “Investigation of laser induced breakdown spectroscopy (libs) for the differentiation of nerve and gland tissue–a possible application for a laser surgery feedback control mechanism,” Plasma Sci. Technol. 18(6), 654–660 (2016). [CrossRef]  

26. M. E. Brezinski, G. J. Tearney, B. E. Bouma, J. A. Izatt, M. R. Hee, E. A. Swanson, J. F. Southern, and J. G. Fujimoto, “Optical coherence tomography for optical biopsy,” Circulation 93(6), 1206–1213 (1996). [CrossRef]  

27. L. M. B. Bernal, I. T. Schmidt, N. Vulin, J. Widmer, J. G. Snedeker, P. C. Cattin, A. Zam, and G. Rauter, “Optimizing controlled laser cutting of hard tissue (bone),” at - Autom. 66(12), 1072–1082 (2018). [CrossRef]  

28. A. Hamidi, Y. A. Bayhaqi, F. Canbaz, A. A. Navarini, P. C. Cattin, and A. Zam, “Long-range optical coherence tomography with extended depth-of-focus: avisual feedback system for smart laser osteotomy,” Biomed. Opt. Express 12(4), 2118–2133 (2021). [CrossRef]  

29. N. Katta, A. D. Estrada, A. B. McElroy, A. Gruslova, M. Oglesby, A. G. Cabe, M. D. Feldman, R. D. Fleming, A. J. Brenner, and T. E. Milner, “Laser brain cancer surgery in a xenograft model guided by optical coherence tomography,” Theranostics 9(12), 3555–3564 (2019). [CrossRef]  

30. Y. Fan, B. Zhang, W. Chang, X. Zhang, and H. Liao, “A novel integration of spectral-domain optical-coherence-tomography and laser-ablation system for precision treatment,” Int. J. Comput. Assist. Radiol. Surg. 13(3), 411–423 (2018). [CrossRef]  

31. F.-Y. Chang, M.-T. Tsai, Z.-Y. Wang, C.-K. Chi, C.-K. Lee, C.-H. Yang, M.-C. Chan, and Y.-J. Lee, “Optical coherence tomography-guided laser microsurgery for blood coagulation with continuous-wave laser diode,” Sci. Rep. 5(1), 16739 (2015). [CrossRef]  

32. G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’Hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. Express 4(7), 1014–1130 (2013). [CrossRef]  

33. S. Liu, Y. Sotomi, J. Eggermont, G. Nakazawa, S. Torii, T. Ijichi, Y. Onuma, P. W. Serruys, B. P. F. Lelieveldt, and J. Dijkstra, “Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images,” J. Biomed. Opt. 22(09), 1–16 (2017). [CrossRef]  

34. L. S. Athanasiou, C. V. Bourantas, G. Rigas, A. I. Sakellarios, T. P. Exarchos, P. K. Siogkas, A. Ricciardi, K. K. Naka, M. I. Papafaklis, L. K. Michalis, F. Prati, and D. I. Fotiadis, “Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images,” J. Biomed. Opt. 19(2), 026009 (2014). [CrossRef]  

35. J. Yang, B. Zhang, H. Wang, F. Lin, Y. Han, and X. Liu, “Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography,” Biocybern. Biomed. Eng. 39(3), 719–727 (2019). [CrossRef]  

36. R. Shalev, D. Nakamura, S. Nishino, A. Rollins, H. Bezerra, D. Wilson, and S. Ray, “Automated volumetric intravascular plaque classification using optical coherence tomography,” AI Magazine 38(1), 61–72 (2017). [CrossRef]  

37. A. Abdolmanafi, L. Duong, N. Dahdah, I. R. Adib, and F. Cheriet, “Characterization of coronary artery pathological formations from oct imaging using deep learning,” Biomed. Opt. Express 9(10), 4936–4960 (2018). [CrossRef]  

38. N. Gessert, M. Lutz, M. Heyder, S. Latus, D. M. Leistner, Y. S. Abdelwahed, and A. Schlaefer, “Automatic plaque detection in ivoct pullbacks using convolutional neural networks,” IEEE Transactions on Med. Imaging 38(2), 426–434 (2019). [CrossRef]  

39. J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–00 (1999). [CrossRef]  

40. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis Mach. Intell. PAMI-8(6), 679–698 (1986). [CrossRef]  

41. S. Singh and R. Singh, “Comparison of various edge detection techniques,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), (2015), pp. 393–396.

42. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017). [CrossRef]  

43. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015, Y. Bengio and Y. LeCun, eds. (2015).

44. K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision - ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling, eds. (Springer International Publishing, 2016), pp. 630–645.

45. G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 2261–2269.

46. J. J. Rico-Jimenez, D. U. Campos-Delgado, M. Villiger, K. Otsuka, B. E. Bouma, and J. A. Jo, “Automatic classification of atherosclerotic plaques imaged with intravascular OCT,” Biomed. Opt. Express 7(10), 4069–4085 (2016). [CrossRef]  

47. K. A. Vermeer, J. Mo, J. J. A. Weda, H. G. Lemij, and J. F. de Boer, “Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography,” Biomed. Opt. Express 5(1), 322–337 (2014). [CrossRef]  

48. F. Chollet, “Keras,” (2015). https://keras.io.

49. D. Hand and P. Christen, “A note on using the f-measure for evaluating record linkage algorithms,” Stat. Comput. 28(3), 539–547 (2018). [CrossRef]  

50. S. Huang, C. Tang, M. Xu, Y. Qiu, and Z. Lei, “Bm3d-based total variation algorithm for speckle removal with structure-preserving in oct images,” Appl. Opt. 58(23), 6233–6243 (2019). [CrossRef]  

51. H. Liu, S. Lin, C. Ye, D. Yu, J. Qin, and L. An, “Using a dual-tree complex wavelet transform for denoising an optical coherence tomography angiography blood vessel image,” OSA Continuum 3(9), 2630–2645 (2020). [CrossRef]  

52. K. J. Halupka, B. J. Antony, M. H. Lee, K. A. Lucy, R. S. Rai, H. Ishikawa, G. Wollstein, J. S. Schuman, and R. Garnavi, “Retinal optical coherence tomography image enhancement via deep learning,” Biomed. Opt. Express 9(12), 6205–6221 (2018). [CrossRef]  

References

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  11. P. C. Ashok, M. E. Giardini, K. Dholakia, and W. Sibbett, “A raman spectroscopy bio-sensor for tissue discrimination in surgical robotics,” J. Biophotonics 7(1-2), 103–109 (2014).
    [Crossref]
  12. F. Stelzle, M. Rohde, M. Riemann, N. Oetter, W. Adler, K. Tangermann-Gerk, M. Schmidt, and C. Knipfer, “Autofluorescence spectroscopy for nerve-sparing laser surgery of the head and neck–the influence of laser-tissue interaction,” Lasers Med. Sci. 32(6), 1289–1300 (2017).
    [Crossref]
  13. F. Stelzle, C. Knipfer, W. Adler, M. Rohde, N. Oetter, E. Nkenke, M. Schmidt, and K. Tangermann-Gerk, “Tissue discrimination by uncorrected autofluorescence spectra: A proof-of-principle study for tissue-specific laser surgery,” Sensors 13(10), 13717–13731 (2013).
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  14. A. Zam, J. Franke, and M. Merklein, Optical Tissue Differentiation for Sensor-controlled Tissue-specific Laser Surgery (Meisenbach, 2011).
  15. R. Gunaratne, J. Goncalves, I. Monteath, R. Sheh, M. Kapfer, R. Chipper, B. Robertson, R. Khan, D. Fick, and C. N. Ironside, “Wavelength weightings in machine learning for ovine joint tissue differentiation using diffuse reflectance spectroscopy (drs),” Biomed. Opt. Express 11(9), 5122–5131 (2020).
    [Crossref]
  16. F. Fanjul-Vélez, S. Pampín-Suárez, and J. L. Arce-Diego, “Application of classification algorithms to diffuse reflectance spectroscopy measurements for ex vivo characterization of biological tissues,” Entropy 22(7), 736 (2020).
    [Crossref]
  17. R. Gunaratne, I. Monteath, J. Goncalves, R. Sheh, C. N. Ironside, M. Kapfer, R. Chipper, B. Robertson, R. Khan, and D. Fick, “Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data,” Biomed. Opt. Express 10(8), 3889–3898 (2019).
    [Crossref]
  18. A. Zam, F. Stelzle, K. Tangermann-Gerk, W. Adler, E. Nkenke, F. W. Neukam, M. Schmidt, and A. Douplik, “In vivo soft tissue differentiation by diffuse reflectance spectroscopy: preliminary results,” Phys. Procedia 5, 655–658 (2010).
    [Crossref]
  19. F. Stelzle, K. Tangermann-Gerk, W. Adler, A. Zam, M. Schmidt, A. Douplik, and E. Nkenke, “Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery,” Lasers Surg. Med. 42(4), 319–325 (2010).
    [Crossref]
  20. H. Nguendon Kenhagho, G. Rauter, R. Guzman, P. C. Cattin, and A. Zam, “Optoacoustic tissue differentiation using a mach-zehnder interferometer,” IEEE Transactions on Ultrason. Ferroelectr. Freq. Control. 66(9), 1435–1443 (2019).
    [Crossref]
  21. E. Bay, A. Douplik, and D. Razansky, “Optoacoustic monitoring of cutting efficiency and thermal damage during laser ablation,” Lasers Med. Sci. 29(3), 1029–1035 (2014).
    [Crossref]
  22. V. Periyasamy, C. Özsoy, M. Reiss, X. L. Deán-Ben, and D. Razansky, “In vivo optoacoustic monitoring of percutaneous laser ablation of tumors in a murine breast cancer model,” Opt. Lett. 45(7), 2006–2009 (2020).
    [Crossref]
  23. H. Abbasi, L. M. B. Bernal, A. Hamidi, A. Droneau, F. Canbaz, R. Guzman, S. L. Jacques, P. C. Cattin, and A. Zam, “Combined nd:yag and er:yag lasers for real-time closed-loop tissue-specific laser osteotomy,” Biomed. Opt. Express 11(4), 1790–1807 (2020).
    [Crossref]
  24. R. Kanawade, F. Mahari, F. Klämpfl, M. Rohde, C. Knipfer, K. Tangermann-Gerk, W. Adler, M. Schmidt, and F. Stelzle, “Qualitative tissue differentiation by analysing the intensity ratios of atomic emission lines using laser induced breakdown spectroscopy (libs): prospects for a feedback mechanism for surgical laser systems,” J. Biophotonics 8(1-2), 153–161 (2015).
    [Crossref]
  25. F. Mehari, M. Rohde, C. Knipfer, R. Kanawade, F. Klämpfl, W. Adler, N. Oetter, F. Stelzle, and M. Schmidt, “Investigation of laser induced breakdown spectroscopy (libs) for the differentiation of nerve and gland tissue–a possible application for a laser surgery feedback control mechanism,” Plasma Sci. Technol. 18(6), 654–660 (2016).
    [Crossref]
  26. M. E. Brezinski, G. J. Tearney, B. E. Bouma, J. A. Izatt, M. R. Hee, E. A. Swanson, J. F. Southern, and J. G. Fujimoto, “Optical coherence tomography for optical biopsy,” Circulation 93(6), 1206–1213 (1996).
    [Crossref]
  27. L. M. B. Bernal, I. T. Schmidt, N. Vulin, J. Widmer, J. G. Snedeker, P. C. Cattin, A. Zam, and G. Rauter, “Optimizing controlled laser cutting of hard tissue (bone),” at - Autom. 66(12), 1072–1082 (2018).
    [Crossref]
  28. A. Hamidi, Y. A. Bayhaqi, F. Canbaz, A. A. Navarini, P. C. Cattin, and A. Zam, “Long-range optical coherence tomography with extended depth-of-focus: avisual feedback system for smart laser osteotomy,” Biomed. Opt. Express 12(4), 2118–2133 (2021).
    [Crossref]
  29. N. Katta, A. D. Estrada, A. B. McElroy, A. Gruslova, M. Oglesby, A. G. Cabe, M. D. Feldman, R. D. Fleming, A. J. Brenner, and T. E. Milner, “Laser brain cancer surgery in a xenograft model guided by optical coherence tomography,” Theranostics 9(12), 3555–3564 (2019).
    [Crossref]
  30. Y. Fan, B. Zhang, W. Chang, X. Zhang, and H. Liao, “A novel integration of spectral-domain optical-coherence-tomography and laser-ablation system for precision treatment,” Int. J. Comput. Assist. Radiol. Surg. 13(3), 411–423 (2018).
    [Crossref]
  31. F.-Y. Chang, M.-T. Tsai, Z.-Y. Wang, C.-K. Chi, C.-K. Lee, C.-H. Yang, M.-C. Chan, and Y.-J. Lee, “Optical coherence tomography-guided laser microsurgery for blood coagulation with continuous-wave laser diode,” Sci. Rep. 5(1), 16739 (2015).
    [Crossref]
  32. G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’Hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. Express 4(7), 1014–1130 (2013).
    [Crossref]
  33. S. Liu, Y. Sotomi, J. Eggermont, G. Nakazawa, S. Torii, T. Ijichi, Y. Onuma, P. W. Serruys, B. P. F. Lelieveldt, and J. Dijkstra, “Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images,” J. Biomed. Opt. 22(09), 1–16 (2017).
    [Crossref]
  34. L. S. Athanasiou, C. V. Bourantas, G. Rigas, A. I. Sakellarios, T. P. Exarchos, P. K. Siogkas, A. Ricciardi, K. K. Naka, M. I. Papafaklis, L. K. Michalis, F. Prati, and D. I. Fotiadis, “Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images,” J. Biomed. Opt. 19(2), 026009 (2014).
    [Crossref]
  35. J. Yang, B. Zhang, H. Wang, F. Lin, Y. Han, and X. Liu, “Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography,” Biocybern. Biomed. Eng. 39(3), 719–727 (2019).
    [Crossref]
  36. R. Shalev, D. Nakamura, S. Nishino, A. Rollins, H. Bezerra, D. Wilson, and S. Ray, “Automated volumetric intravascular plaque classification using optical coherence tomography,” AI Magazine 38(1), 61–72 (2017).
    [Crossref]
  37. A. Abdolmanafi, L. Duong, N. Dahdah, I. R. Adib, and F. Cheriet, “Characterization of coronary artery pathological formations from oct imaging using deep learning,” Biomed. Opt. Express 9(10), 4936–4960 (2018).
    [Crossref]
  38. N. Gessert, M. Lutz, M. Heyder, S. Latus, D. M. Leistner, Y. S. Abdelwahed, and A. Schlaefer, “Automatic plaque detection in ivoct pullbacks using convolutional neural networks,” IEEE Transactions on Med. Imaging 38(2), 426–434 (2019).
    [Crossref]
  39. J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–00 (1999).
    [Crossref]
  40. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis Mach. Intell. PAMI-8(6), 679–698 (1986).
    [Crossref]
  41. S. Singh and R. Singh, “Comparison of various edge detection techniques,” in 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), (2015), pp. 393–396.
  42. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017).
    [Crossref]
  43. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015, Y. Bengio and Y. LeCun, eds. (2015).
  44. K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision - ECCV 2016, B. Leibe, J. Matas, N. Sebe, and M. Welling, eds. (Springer International Publishing, 2016), pp. 630–645.
  45. G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 2261–2269.
  46. J. J. Rico-Jimenez, D. U. Campos-Delgado, M. Villiger, K. Otsuka, B. E. Bouma, and J. A. Jo, “Automatic classification of atherosclerotic plaques imaged with intravascular OCT,” Biomed. Opt. Express 7(10), 4069–4085 (2016).
    [Crossref]
  47. K. A. Vermeer, J. Mo, J. J. A. Weda, H. G. Lemij, and J. F. de Boer, “Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography,” Biomed. Opt. Express 5(1), 322–337 (2014).
    [Crossref]
  48. F. Chollet, “Keras,” (2015). https://keras.io .
  49. D. Hand and P. Christen, “A note on using the f-measure for evaluating record linkage algorithms,” Stat. Comput. 28(3), 539–547 (2018).
    [Crossref]
  50. S. Huang, C. Tang, M. Xu, Y. Qiu, and Z. Lei, “Bm3d-based total variation algorithm for speckle removal with structure-preserving in oct images,” Appl. Opt. 58(23), 6233–6243 (2019).
    [Crossref]
  51. H. Liu, S. Lin, C. Ye, D. Yu, J. Qin, and L. An, “Using a dual-tree complex wavelet transform for denoising an optical coherence tomography angiography blood vessel image,” OSA Continuum 3(9), 2630–2645 (2020).
    [Crossref]
  52. K. J. Halupka, B. J. Antony, M. H. Lee, K. A. Lucy, R. S. Rai, H. Ishikawa, G. Wollstein, J. S. Schuman, and R. Garnavi, “Retinal optical coherence tomography image enhancement via deep learning,” Biomed. Opt. Express 9(12), 6205–6221 (2018).
    [Crossref]

2021 (2)

C. Duverney, H. Abbasi, M. Berkelaar, K. Pelttari, P. C. Cattin, A. Barbero, A. Zam, and G. Rauter, “Sterile tissue ablation using laser light–system design, experimental validation, and outlook on clinical applicability,” J. Med. Devices 15(1), 011104 (2021).
[Crossref]

A. Hamidi, Y. A. Bayhaqi, F. Canbaz, A. A. Navarini, P. C. Cattin, and A. Zam, “Long-range optical coherence tomography with extended depth-of-focus: avisual feedback system for smart laser osteotomy,” Biomed. Opt. Express 12(4), 2118–2133 (2021).
[Crossref]

2020 (6)

2019 (6)

S. Huang, C. Tang, M. Xu, Y. Qiu, and Z. Lei, “Bm3d-based total variation algorithm for speckle removal with structure-preserving in oct images,” Appl. Opt. 58(23), 6233–6243 (2019).
[Crossref]

R. Gunaratne, I. Monteath, J. Goncalves, R. Sheh, C. N. Ironside, M. Kapfer, R. Chipper, B. Robertson, R. Khan, and D. Fick, “Machine learning classification of human joint tissue from diffuse reflectance spectroscopy data,” Biomed. Opt. Express 10(8), 3889–3898 (2019).
[Crossref]

H. Nguendon Kenhagho, G. Rauter, R. Guzman, P. C. Cattin, and A. Zam, “Optoacoustic tissue differentiation using a mach-zehnder interferometer,” IEEE Transactions on Ultrason. Ferroelectr. Freq. Control. 66(9), 1435–1443 (2019).
[Crossref]

N. Katta, A. D. Estrada, A. B. McElroy, A. Gruslova, M. Oglesby, A. G. Cabe, M. D. Feldman, R. D. Fleming, A. J. Brenner, and T. E. Milner, “Laser brain cancer surgery in a xenograft model guided by optical coherence tomography,” Theranostics 9(12), 3555–3564 (2019).
[Crossref]

J. Yang, B. Zhang, H. Wang, F. Lin, Y. Han, and X. Liu, “Automated characterization and classification of coronary atherosclerotic plaques for intravascular optical coherence tomography,” Biocybern. Biomed. Eng. 39(3), 719–727 (2019).
[Crossref]

N. Gessert, M. Lutz, M. Heyder, S. Latus, D. M. Leistner, Y. S. Abdelwahed, and A. Schlaefer, “Automatic plaque detection in ivoct pullbacks using convolutional neural networks,” IEEE Transactions on Med. Imaging 38(2), 426–434 (2019).
[Crossref]

2018 (6)

L. M. B. Bernal, I. T. Schmidt, N. Vulin, J. Widmer, J. G. Snedeker, P. C. Cattin, A. Zam, and G. Rauter, “Optimizing controlled laser cutting of hard tissue (bone),” at - Autom. 66(12), 1072–1082 (2018).
[Crossref]

Y. Fan, B. Zhang, W. Chang, X. Zhang, and H. Liao, “A novel integration of spectral-domain optical-coherence-tomography and laser-ablation system for precision treatment,” Int. J. Comput. Assist. Radiol. Surg. 13(3), 411–423 (2018).
[Crossref]

E. Cordero, I. Latka, C. Matthäus, I. Schie, and J. Popp, “In-vivo raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

K. J. Halupka, B. J. Antony, M. H. Lee, K. A. Lucy, R. S. Rai, H. Ishikawa, G. Wollstein, J. S. Schuman, and R. Garnavi, “Retinal optical coherence tomography image enhancement via deep learning,” Biomed. Opt. Express 9(12), 6205–6221 (2018).
[Crossref]

A. Abdolmanafi, L. Duong, N. Dahdah, I. R. Adib, and F. Cheriet, “Characterization of coronary artery pathological formations from oct imaging using deep learning,” Biomed. Opt. Express 9(10), 4936–4960 (2018).
[Crossref]

D. Hand and P. Christen, “A note on using the f-measure for evaluating record linkage algorithms,” Stat. Comput. 28(3), 539–547 (2018).
[Crossref]

2017 (5)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

J. Dabis, O. Templeton-Ward, A. E. Lacey, B. Narayan, and A. Trompeter, “The history, evolution and basic science of osteotomy techniques,” Strateg. Trauma Limb Reconstr. 12(3), 169–180 (2017).
[Crossref]

F. Stelzle, M. Rohde, M. Riemann, N. Oetter, W. Adler, K. Tangermann-Gerk, M. Schmidt, and C. Knipfer, “Autofluorescence spectroscopy for nerve-sparing laser surgery of the head and neck–the influence of laser-tissue interaction,” Lasers Med. Sci. 32(6), 1289–1300 (2017).
[Crossref]

S. Liu, Y. Sotomi, J. Eggermont, G. Nakazawa, S. Torii, T. Ijichi, Y. Onuma, P. W. Serruys, B. P. F. Lelieveldt, and J. Dijkstra, “Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images,” J. Biomed. Opt. 22(09), 1–16 (2017).
[Crossref]

R. Shalev, D. Nakamura, S. Nishino, A. Rollins, H. Bezerra, D. Wilson, and S. Ray, “Automated volumetric intravascular plaque classification using optical coherence tomography,” AI Magazine 38(1), 61–72 (2017).
[Crossref]

2016 (2)

F. Mehari, M. Rohde, C. Knipfer, R. Kanawade, F. Klämpfl, W. Adler, N. Oetter, F. Stelzle, and M. Schmidt, “Investigation of laser induced breakdown spectroscopy (libs) for the differentiation of nerve and gland tissue–a possible application for a laser surgery feedback control mechanism,” Plasma Sci. Technol. 18(6), 654–660 (2016).
[Crossref]

J. J. Rico-Jimenez, D. U. Campos-Delgado, M. Villiger, K. Otsuka, B. E. Bouma, and J. A. Jo, “Automatic classification of atherosclerotic plaques imaged with intravascular OCT,” Biomed. Opt. Express 7(10), 4069–4085 (2016).
[Crossref]

2015 (3)

R. Kanawade, F. Mahari, F. Klämpfl, M. Rohde, C. Knipfer, K. Tangermann-Gerk, W. Adler, M. Schmidt, and F. Stelzle, “Qualitative tissue differentiation by analysing the intensity ratios of atomic emission lines using laser induced breakdown spectroscopy (libs): prospects for a feedback mechanism for surgical laser systems,” J. Biophotonics 8(1-2), 153–161 (2015).
[Crossref]

F.-Y. Chang, M.-T. Tsai, Z.-Y. Wang, C.-K. Chi, C.-K. Lee, C.-H. Yang, M.-C. Chan, and Y.-J. Lee, “Optical coherence tomography-guided laser microsurgery for blood coagulation with continuous-wave laser diode,” Sci. Rep. 5(1), 16739 (2015).
[Crossref]

K.-W. Baek, W. Deibel, D. Marinov, M. Griessen, M. Dard, A. Bruno, H.-F. Zeilhofer, P. Cattin, and P. Juergens, “A comparative investigation of bone surface after cutting with mechanical tools and er:yag laser,” Lasers Surg. Med. 47(5), 426–432 (2015).
[Crossref]

2014 (4)

P. C. Ashok, M. E. Giardini, K. Dholakia, and W. Sibbett, “A raman spectroscopy bio-sensor for tissue discrimination in surgical robotics,” J. Biophotonics 7(1-2), 103–109 (2014).
[Crossref]

E. Bay, A. Douplik, and D. Razansky, “Optoacoustic monitoring of cutting efficiency and thermal damage during laser ablation,” Lasers Med. Sci. 29(3), 1029–1035 (2014).
[Crossref]

L. S. Athanasiou, C. V. Bourantas, G. Rigas, A. I. Sakellarios, T. P. Exarchos, P. K. Siogkas, A. Ricciardi, K. K. Naka, M. I. Papafaklis, L. K. Michalis, F. Prati, and D. I. Fotiadis, “Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images,” J. Biomed. Opt. 19(2), 026009 (2014).
[Crossref]

K. A. Vermeer, J. Mo, J. J. A. Weda, H. G. Lemij, and J. F. de Boer, “Depth-resolved model-based reconstruction of attenuation coefficients in optical coherence tomography,” Biomed. Opt. Express 5(1), 322–337 (2014).
[Crossref]

2013 (2)

G. J. Ughi, T. Adriaenssens, P. Sinnaeve, W. Desmet, and J. D’Hooge, “Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images,” Biomed. Opt. Express 4(7), 1014–1130 (2013).
[Crossref]

F. Stelzle, C. Knipfer, W. Adler, M. Rohde, N. Oetter, E. Nkenke, M. Schmidt, and K. Tangermann-Gerk, “Tissue discrimination by uncorrected autofluorescence spectra: A proof-of-principle study for tissue-specific laser surgery,” Sensors 13(10), 13717–13731 (2013).
[Crossref]

2010 (3)

A. Zam, F. Stelzle, K. Tangermann-Gerk, W. Adler, E. Nkenke, F. W. Neukam, M. Schmidt, and A. Douplik, “In vivo soft tissue differentiation by diffuse reflectance spectroscopy: preliminary results,” Phys. Procedia 5, 655–658 (2010).
[Crossref]

F. Stelzle, K. Tangermann-Gerk, W. Adler, A. Zam, M. Schmidt, A. Douplik, and E. Nkenke, “Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery,” Lasers Surg. Med. 42(4), 319–325 (2010).
[Crossref]

S. Stübinger, “Advances in bone surgery: the er:yag laser in oral surgery and implant dentistry,” Clin., Cosmet. Invest. Dent. 2, 47–62 (2010).
[Crossref]

2000 (1)

S. Kondo, Y. Okada, H. Iseki, T. Hori, K. Takakura, A. Kobayashi, and H. Nagata, “Thermological study of drilling bone tissue with a high-speed drill,” Neurosurgery 46(5), 1162–1168 (2000).
[Crossref]

1999 (1)

J. Schmitt, S. Xiang, and K. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–00 (1999).
[Crossref]

1997 (1)

H. Yamaguchi, K. Kobayashi, R. Osada, E.-i. Sakuraba, T. Nomura, T. Arai, and J. Nakamura, “Effects of irradiation of an erbium: Yag laser on root surfaces,” J. Periodontol. 68(12), 1151–1155 (1997).
[Crossref]

1996 (2)

M. E. Brezinski, G. J. Tearney, B. E. Bouma, J. A. Izatt, M. R. Hee, E. A. Swanson, J. F. Southern, and J. G. Fujimoto, “Optical coherence tomography for optical biopsy,” Circulation 93(6), 1206–1213 (1996).
[Crossref]

Y. Ando, A. Aoki, H. Watanabe, and I. Ishikawa, “Bactericidal effect of erbium yag laser on periodontopathic bacteria,” Lasers Surg. Med. 19(2), 190–200 (1996).
[Crossref]

1993 (1)

M. Wilson, “Photolysis of oral bacteria and its potential use in the treatment of caries and periodontal disease,” J. Appl. Bacteriol. 75(4), 299–306 (1993).
[Crossref]

1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis Mach. Intell. PAMI-8(6), 679–698 (1986).
[Crossref]

Abbasi, H.

C. Duverney, H. Abbasi, M. Berkelaar, K. Pelttari, P. C. Cattin, A. Barbero, A. Zam, and G. Rauter, “Sterile tissue ablation using laser light–system design, experimental validation, and outlook on clinical applicability,” J. Med. Devices 15(1), 011104 (2021).
[Crossref]

H. Abbasi, L. M. B. Bernal, A. Hamidi, A. Droneau, F. Canbaz, R. Guzman, S. L. Jacques, P. C. Cattin, and A. Zam, “Combined nd:yag and er:yag lasers for real-time closed-loop tissue-specific laser osteotomy,” Biomed. Opt. Express 11(4), 1790–1807 (2020).
[Crossref]

Abdelwahed, Y. S.

N. Gessert, M. Lutz, M. Heyder, S. Latus, D. M. Leistner, Y. S. Abdelwahed, and A. Schlaefer, “Automatic plaque detection in ivoct pullbacks using convolutional neural networks,” IEEE Transactions on Med. Imaging 38(2), 426–434 (2019).
[Crossref]

Abdolmanafi, A.

Adib, I. R.

Adler, W.

F. Stelzle, M. Rohde, M. Riemann, N. Oetter, W. Adler, K. Tangermann-Gerk, M. Schmidt, and C. Knipfer, “Autofluorescence spectroscopy for nerve-sparing laser surgery of the head and neck–the influence of laser-tissue interaction,” Lasers Med. Sci. 32(6), 1289–1300 (2017).
[Crossref]

F. Mehari, M. Rohde, C. Knipfer, R. Kanawade, F. Klämpfl, W. Adler, N. Oetter, F. Stelzle, and M. Schmidt, “Investigation of laser induced breakdown spectroscopy (libs) for the differentiation of nerve and gland tissue–a possible application for a laser surgery feedback control mechanism,” Plasma Sci. Technol. 18(6), 654–660 (2016).
[Crossref]

R. Kanawade, F. Mahari, F. Klämpfl, M. Rohde, C. Knipfer, K. Tangermann-Gerk, W. Adler, M. Schmidt, and F. Stelzle, “Qualitative tissue differentiation by analysing the intensity ratios of atomic emission lines using laser induced breakdown spectroscopy (libs): prospects for a feedback mechanism for surgical laser systems,” J. Biophotonics 8(1-2), 153–161 (2015).
[Crossref]

F. Stelzle, C. Knipfer, W. Adler, M. Rohde, N. Oetter, E. Nkenke, M. Schmidt, and K. Tangermann-Gerk, “Tissue discrimination by uncorrected autofluorescence spectra: A proof-of-principle study for tissue-specific laser surgery,” Sensors 13(10), 13717–13731 (2013).
[Crossref]

A. Zam, F. Stelzle, K. Tangermann-Gerk, W. Adler, E. Nkenke, F. W. Neukam, M. Schmidt, and A. Douplik, “In vivo soft tissue differentiation by diffuse reflectance spectroscopy: preliminary results,” Phys. Procedia 5, 655–658 (2010).
[Crossref]

F. Stelzle, K. Tangermann-Gerk, W. Adler, A. Zam, M. Schmidt, A. Douplik, and E. Nkenke, “Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery,” Lasers Surg. Med. 42(4), 319–325 (2010).
[Crossref]

Adriaenssens, T.

An, L.

Ando, Y.

Y. Ando, A. Aoki, H. Watanabe, and I. Ishikawa, “Bactericidal effect of erbium yag laser on periodontopathic bacteria,” Lasers Surg. Med. 19(2), 190–200 (1996).
[Crossref]

Antony, B. J.

Aoki, A.

Y. Ando, A. Aoki, H. Watanabe, and I. Ishikawa, “Bactericidal effect of erbium yag laser on periodontopathic bacteria,” Lasers Surg. Med. 19(2), 190–200 (1996).
[Crossref]

Arai, T.

H. Yamaguchi, K. Kobayashi, R. Osada, E.-i. Sakuraba, T. Nomura, T. Arai, and J. Nakamura, “Effects of irradiation of an erbium: Yag laser on root surfaces,” J. Periodontol. 68(12), 1151–1155 (1997).
[Crossref]

Arce-Diego, J. L.

F. Fanjul-Vélez, S. Pampín-Suárez, and J. L. Arce-Diego, “Application of classification algorithms to diffuse reflectance spectroscopy measurements for ex vivo characterization of biological tissues,” Entropy 22(7), 736 (2020).
[Crossref]

Ashok, P. C.

P. C. Ashok, M. E. Giardini, K. Dholakia, and W. Sibbett, “A raman spectroscopy bio-sensor for tissue discrimination in surgical robotics,” J. Biophotonics 7(1-2), 103–109 (2014).
[Crossref]

Athanasiou, L. S.

L. S. Athanasiou, C. V. Bourantas, G. Rigas, A. I. Sakellarios, T. P. Exarchos, P. K. Siogkas, A. Ricciardi, K. K. Naka, M. I. Papafaklis, L. K. Michalis, F. Prati, and D. I. Fotiadis, “Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images,” J. Biomed. Opt. 19(2), 026009 (2014).
[Crossref]

Baek, K.-W.

K.-W. Baek, W. Deibel, D. Marinov, M. Griessen, M. Dard, A. Bruno, H.-F. Zeilhofer, P. Cattin, and P. Juergens, “A comparative investigation of bone surface after cutting with mechanical tools and er:yag laser,” Lasers Surg. Med. 47(5), 426–432 (2015).
[Crossref]

Barbero, A.

C. Duverney, H. Abbasi, M. Berkelaar, K. Pelttari, P. C. Cattin, A. Barbero, A. Zam, and G. Rauter, “Sterile tissue ablation using laser light–system design, experimental validation, and outlook on clinical applicability,” J. Med. Devices 15(1), 011104 (2021).
[Crossref]

Bay, E.

E. Bay, A. Douplik, and D. Razansky, “Optoacoustic monitoring of cutting efficiency and thermal damage during laser ablation,” Lasers Med. Sci. 29(3), 1029–1035 (2014).
[Crossref]

Bayhaqi, Y. A.

Berkelaar, M.

C. Duverney, H. Abbasi, M. Berkelaar, K. Pelttari, P. C. Cattin, A. Barbero, A. Zam, and G. Rauter, “Sterile tissue ablation using laser light–system design, experimental validation, and outlook on clinical applicability,” J. Med. Devices 15(1), 011104 (2021).
[Crossref]

Bernal, L. M. B.

H. Abbasi, L. M. B. Bernal, A. Hamidi, A. Droneau, F. Canbaz, R. Guzman, S. L. Jacques, P. C. Cattin, and A. Zam, “Combined nd:yag and er:yag lasers for real-time closed-loop tissue-specific laser osteotomy,” Biomed. Opt. Express 11(4), 1790–1807 (2020).
[Crossref]

L. M. B. Bernal, I. T. Schmidt, N. Vulin, J. Widmer, J. G. Snedeker, P. C. Cattin, A. Zam, and G. Rauter, “Optimizing controlled laser cutting of hard tissue (bone),” at - Autom. 66(12), 1072–1082 (2018).
[Crossref]

Bezerra, H.

R. Shalev, D. Nakamura, S. Nishino, A. Rollins, H. Bezerra, D. Wilson, and S. Ray, “Automated volumetric intravascular plaque classification using optical coherence tomography,” AI Magazine 38(1), 61–72 (2017).
[Crossref]

Bouma, B. E.

J. J. Rico-Jimenez, D. U. Campos-Delgado, M. Villiger, K. Otsuka, B. E. Bouma, and J. A. Jo, “Automatic classification of atherosclerotic plaques imaged with intravascular OCT,” Biomed. Opt. Express 7(10), 4069–4085 (2016).
[Crossref]

M. E. Brezinski, G. J. Tearney, B. E. Bouma, J. A. Izatt, M. R. Hee, E. A. Swanson, J. F. Southern, and J. G. Fujimoto, “Optical coherence tomography for optical biopsy,” Circulation 93(6), 1206–1213 (1996).
[Crossref]

Bourantas, C. V.

L. S. Athanasiou, C. V. Bourantas, G. Rigas, A. I. Sakellarios, T. P. Exarchos, P. K. Siogkas, A. Ricciardi, K. K. Naka, M. I. Papafaklis, L. K. Michalis, F. Prati, and D. I. Fotiadis, “Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images,” J. Biomed. Opt. 19(2), 026009 (2014).
[Crossref]

Brenner, A. J.

N. Katta, A. D. Estrada, A. B. McElroy, A. Gruslova, M. Oglesby, A. G. Cabe, M. D. Feldman, R. D. Fleming, A. J. Brenner, and T. E. Milner, “Laser brain cancer surgery in a xenograft model guided by optical coherence tomography,” Theranostics 9(12), 3555–3564 (2019).
[Crossref]

Brezinski, M. E.

M. E. Brezinski, G. J. Tearney, B. E. Bouma, J. A. Izatt, M. R. Hee, E. A. Swanson, J. F. Southern, and J. G. Fujimoto, “Optical coherence tomography for optical biopsy,” Circulation 93(6), 1206–1213 (1996).
[Crossref]

Bruno, A.

K.-W. Baek, W. Deibel, D. Marinov, M. Griessen, M. Dard, A. Bruno, H.-F. Zeilhofer, P. Cattin, and P. Juergens, “A comparative investigation of bone surface after cutting with mechanical tools and er:yag laser,” Lasers Surg. Med. 47(5), 426–432 (2015).
[Crossref]

Cabe, A. G.

N. Katta, A. D. Estrada, A. B. McElroy, A. Gruslova, M. Oglesby, A. G. Cabe, M. D. Feldman, R. D. Fleming, A. J. Brenner, and T. E. Milner, “Laser brain cancer surgery in a xenograft model guided by optical coherence tomography,” Theranostics 9(12), 3555–3564 (2019).
[Crossref]

Campos-Delgado, D. U.

Canbaz, F.

Canny, J.

J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis Mach. Intell. PAMI-8(6), 679–698 (1986).
[Crossref]

Cattin, P.

K.-W. Baek, W. Deibel, D. Marinov, M. Griessen, M. Dard, A. Bruno, H.-F. Zeilhofer, P. Cattin, and P. Juergens, “A comparative investigation of bone surface after cutting with mechanical tools and er:yag laser,” Lasers Surg. Med. 47(5), 426–432 (2015).
[Crossref]

Cattin, P. C.

C. Duverney, H. Abbasi, M. Berkelaar, K. Pelttari, P. C. Cattin, A. Barbero, A. Zam, and G. Rauter, “Sterile tissue ablation using laser light–system design, experimental validation, and outlook on clinical applicability,” J. Med. Devices 15(1), 011104 (2021).
[Crossref]

A. Hamidi, Y. A. Bayhaqi, F. Canbaz, A. A. Navarini, P. C. Cattin, and A. Zam, “Long-range optical coherence tomography with extended depth-of-focus: avisual feedback system for smart laser osteotomy,” Biomed. Opt. Express 12(4), 2118–2133 (2021).
[Crossref]

H. Abbasi, L. M. B. Bernal, A. Hamidi, A. Droneau, F. Canbaz, R. Guzman, S. L. Jacques, P. C. Cattin, and A. Zam, “Combined nd:yag and er:yag lasers for real-time closed-loop tissue-specific laser osteotomy,” Biomed. Opt. Express 11(4), 1790–1807 (2020).
[Crossref]

H. Nguendon Kenhagho, G. Rauter, R. Guzman, P. C. Cattin, and A. Zam, “Optoacoustic tissue differentiation using a mach-zehnder interferometer,” IEEE Transactions on Ultrason. Ferroelectr. Freq. Control. 66(9), 1435–1443 (2019).
[Crossref]

L. M. B. Bernal, I. T. Schmidt, N. Vulin, J. Widmer, J. G. Snedeker, P. C. Cattin, A. Zam, and G. Rauter, “Optimizing controlled laser cutting of hard tissue (bone),” at - Autom. 66(12), 1072–1082 (2018).
[Crossref]

Chan, M.-C.

F.-Y. Chang, M.-T. Tsai, Z.-Y. Wang, C.-K. Chi, C.-K. Lee, C.-H. Yang, M.-C. Chan, and Y.-J. Lee, “Optical coherence tomography-guided laser microsurgery for blood coagulation with continuous-wave laser diode,” Sci. Rep. 5(1), 16739 (2015).
[Crossref]

Chang, F.-Y.

F.-Y. Chang, M.-T. Tsai, Z.-Y. Wang, C.-K. Chi, C.-K. Lee, C.-H. Yang, M.-C. Chan, and Y.-J. Lee, “Optical coherence tomography-guided laser microsurgery for blood coagulation with continuous-wave laser diode,” Sci. Rep. 5(1), 16739 (2015).
[Crossref]

Chang, W.

Y. Fan, B. Zhang, W. Chang, X. Zhang, and H. Liao, “A novel integration of spectral-domain optical-coherence-tomography and laser-ablation system for precision treatment,” Int. J. Comput. Assist. Radiol. Surg. 13(3), 411–423 (2018).
[Crossref]

Cheriet, F.

Chi, C.-K.

F.-Y. Chang, M.-T. Tsai, Z.-Y. Wang, C.-K. Chi, C.-K. Lee, C.-H. Yang, M.-C. Chan, and Y.-J. Lee, “Optical coherence tomography-guided laser microsurgery for blood coagulation with continuous-wave laser diode,” Sci. Rep. 5(1), 16739 (2015).
[Crossref]

Chipper, R.

Chollet, F.

F. Chollet, “Keras,” (2015). https://keras.io .

Christen, P.

D. Hand and P. Christen, “A note on using the f-measure for evaluating record linkage algorithms,” Stat. Comput. 28(3), 539–547 (2018).
[Crossref]

Cordero, E.

E. Cordero, I. Latka, C. Matthäus, I. Schie, and J. Popp, “In-vivo raman spectroscopy: from basics to applications,” J. Biomed. Opt. 23(07), 1 (2018).
[Crossref]

D’Hooge, J.

Dabis, J.

J. Dabis, O. Templeton-Ward, A. E. Lacey, B. Narayan, and A. Trompeter, “The history, evolution and basic science of osteotomy techniques,” Strateg. Trauma Limb Reconstr. 12(3), 169–180 (2017).
[Crossref]

Dahdah, N.

Dard, M.

K.-W. Baek, W. Deibel, D. Marinov, M. Griessen, M. Dard, A. Bruno, H.-F. Zeilhofer, P. Cattin, and P. Juergens, “A comparative investigation of bone surface after cutting with mechanical tools and er:yag laser,” Lasers Surg. Med. 47(5), 426–432 (2015).
[Crossref]

de Boer, J. F.

Deán-Ben, X. L.

Deibel, W.

K.-W. Baek, W. Deibel, D. Marinov, M. Griessen, M. Dard, A. Bruno, H.-F. Zeilhofer, P. Cattin, and P. Juergens, “A comparative investigation of bone surface after cutting with mechanical tools and er:yag laser,” Lasers Surg. Med. 47(5), 426–432 (2015).
[Crossref]

Desmet, W.

Dholakia, K.

P. C. Ashok, M. E. Giardini, K. Dholakia, and W. Sibbett, “A raman spectroscopy bio-sensor for tissue discrimination in surgical robotics,” J. Biophotonics 7(1-2), 103–109 (2014).
[Crossref]

Dijkstra, J.

S. Liu, Y. Sotomi, J. Eggermont, G. Nakazawa, S. Torii, T. Ijichi, Y. Onuma, P. W. Serruys, B. P. F. Lelieveldt, and J. Dijkstra, “Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images,” J. Biomed. Opt. 22(09), 1–16 (2017).
[Crossref]

Douplik, A.

E. Bay, A. Douplik, and D. Razansky, “Optoacoustic monitoring of cutting efficiency and thermal damage during laser ablation,” Lasers Med. Sci. 29(3), 1029–1035 (2014).
[Crossref]

F. Stelzle, K. Tangermann-Gerk, W. Adler, A. Zam, M. Schmidt, A. Douplik, and E. Nkenke, “Diffuse reflectance spectroscopy for optical soft tissue differentiation as remote feedback control for tissue-specific laser surgery,” Lasers Surg. Med. 42(4), 319–325 (2010).
[Crossref]

A. Zam, F. Stelzle, K. Tangermann-Gerk, W. Adler, E. Nkenke, F. W. Neukam, M. Schmidt, and A. Douplik, “In vivo soft tissue differentiation by diffuse reflectance spectroscopy: preliminary results,” Phys. Procedia 5, 655–658 (2010).
[Crossref]

Droneau, A.

Duong, L.

Duverney, C.

C. Duverney, H. Abbasi, M. Berkelaar, K. Pelttari, P. C. Cattin, A. Barbero, A. Zam, and G. Rauter, “Sterile tissue ablation using laser light–system design, experimental validation, and outlook on clinical applicability,” J. Med. Devices 15(1), 011104 (2021).
[Crossref]

Eggermont, J.

S. Liu, Y. Sotomi, J. Eggermont, G. Nakazawa, S. Torii, T. Ijichi, Y. Onuma, P. W. Serruys, B. P. F. Lelieveldt, and J. Dijkstra, “Tissue characterization with depth-resolved attenuation coefficient and backscatter term in intravascular optical coherence tomography images,” J. Biomed. Opt. 22(09), 1–16 (2017).
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F. Stelzle, M. Rohde, M. Riemann, N. Oetter, W. Adler, K. Tangermann-Gerk, M. Schmidt, and C. Knipfer, “Autofluorescence spectroscopy for nerve-sparing laser surgery of the head and neck–the influence of laser-tissue interaction,” Lasers Med. Sci. 32(6), 1289–1300 (2017).
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Data availability

Data underlying the results presented in this paper are not publicly available at this time for further continuation of the study. However, the data may be obtained from the authors upon reasonable request.

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Figures (6)

Fig. 1.
Fig. 1. Schematic of the proposed OCT-based smart laser surgery system. We used a Fourier-Domain OCT with an Axsun swept-source laser. The OCT laser (red line) is coupled with an ablation laser (blue line) by a dichroic filter. The OCT images are streamed to monitor the ablation process. A region of interest (image patch) from the OCT image is selected on the ablation spot. We trained a convolutional neural network model to identify tissue type based on the extracted image patch. The convolutional neural network’s output provides feedback to an optical shutter and controls the ablation laser to either stop or continue ablation.
Fig. 2.
Fig. 2. Combination-A. The CNN texture and CNN profile models were trained separately. The first model was trained to identify tissue type based on the texture feature. The second CNN model was trained to identify tissue type based on the intensity profile feature in the middle of the image patch. Both of the CNN model’s outputs were then fused with a concatenation layer. The fused feature was connected to a fully connected layer of 4096. At the end layer, a sigmoid activation function was used to identify the tissue type.
Fig. 3.
Fig. 3. Combination-B. The CNN model was trained to differentiate tissue type based on 2-channel image input. This 2-channel image was constructed by combining the image patch and the attenuation patch. The attenuation patch was extracted from the attenuation map at same location with image patch.
Fig. 4.
Fig. 4. Illustration of the femur bone anatomical structure of a pig (left). Cross-cut example of the bone on the diaphysis area is illustrated on the right image.
Fig. 5.
Fig. 5. The left column shows examples of the bone, bone marrow, fat, muscle, and skin tissue samples used in our experiment. The corresponding OCT images (middle column) were scanned on the red line for each tissue sample. The last column (right) shows the attenuation maps reconstructed from the OCT images. The image patches (red box) were taken on the surface of the tissue and used to train the CNN models.
Fig. 6.
Fig. 6. The test confusion matrix of the DenseNet121 models that were trained with the texture feature (a), the profile feature (b), and the attenuation of both features (c). The model trained with texture feature have lower accuracy in classifying bone marrow and fat. On the other hand, the profile and attenuation feature discriminate better for the bone marrow and fat. Therefore, the model has higher accuracy in the combination A and B (d) and (e).

Tables (7)

Tables Icon

Table 1. Comparison of the average  ±  standard deviation of accuracy for the AlexNet and DenseNet models trained with the texture, profile, attenuation, and combinations of features (Combination A and B). The highest average accuracies are highlighted in bold.

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Table 2. Comparison of the average  ±  standard deviation of F1-score for the AlexNet and DenseNet models trained with the texture, profile, attenuation, and combinations of features (Combination A and B). The highest average F1-scores are highlighted in bold.

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Table 3. The average ± standard deviation of the tissues’ attenuation coefficient. The average attenuation coefficient was measured based on the reconstructed attenuation coefficient map patches

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Table 4. Comparison of the average  ±  standard deviation of the prediction time for the AlexNet and DenseNet models trained with the proposed features. The prediction time includes the attenuation coefficient map extraction time for the combination-B model and the model with attenuation maps input. The attenuation coefficient map extraction takes 18.76  ±  1.36 msec. The fastest computation performances are highlighted in bold.

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Table 5. The average  ±  standard deviation of accuracy for all models. The highest average accuracies are highlighted in bold.

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Table 6. The average  ±  standard deviation of F1-score for all models. The highest average F1-scores are highlighted in bold.

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Table 7. Comparison of the average  ±  standard deviation of the prediction time for all models. The prediction time includes the attenuation coefficient map extraction time for the combination-B model and the model with attenuation maps input. The attenuation coefficient map extraction takes 18.76  ±  1.36 msec. The fastest computation performances are highlighted in bold.

Equations (5)

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μ a [ i , j ] I [ i , j ] 2 δ z = j + 1 I [ i , z ] ,
% F 1 s c o r e = T P T P + 1 2 ( F P + F N ) × 100 % ,