Abstract

To investigate the potential of optical coherence tomography (OCT) to distinguish between normal and pathologic thyroid tissue, 3D OCT images were acquired on ex vivo thyroid samples from adult subjects (n=22) diagnosed with a variety of pathologies. The follicular structure was analyzed in terms of count, size, density and sphericity. Results showed that OCT images highly agreed with the corresponding histopatology and the calculated parameters were representative of the follicular structure variation. The analysis of OCT volumes provides quantitative information that could make automatic classification possible. Thus, OCT can be beneficial for intraoperative surgical guidance or in the pathology assessment routine.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2020 (1)

A. Bergenfelz, E. Nordenström, and M. Almquist, “Morbidity in patients with permanent hypoparathyroidism after total thyroidectomy,” Surgery 167(1), 124–128 (2020).
[Crossref]

2019 (3)

R. Ladurner, M. Lerchenberger, N. Al Arabi, J. K. Gallwas, H. Stepp, and K. K. Hallfeldt, “Parathyroid autofluorescence—how does it affect parathyroid and thyroid surgery? a 5 year experience,” Molecules 24(14), 2560 (2019).
[Crossref]

H. Najah and C. Tresallet, “Role of frozen section in the surgical management of indeterminate thyroid nodules,” Gland Surg. 8(S2), S112–S117 (2019).
[Crossref]

H. Wang, D. Won, and S. W. Yoon, “A deep separable neural network for human tissue identification in three-dimensional optical coherence tomography images,” IISE Transactions on Healthc. Syst. Eng. 9(3), 250–271 (2019).
[Crossref]

2018 (5)

M. Treder, J. L. Lauermann, and N. Eter, “Automated detection of exudative age-related macular degeneration in spectral domain optical coherence tomography using deep learning,” Graefe’s Arch. Clin. Exp. Ophthalmol. 256(2), 259–265 (2018).
[Crossref]

C. A. Bollig, D. Lesko, D. Gilley, and L. M. Dooley, “The futility of intraoperative frozen section in the evaluation of follicular thyroid lesions,” Laryngoscope 128(6), 1501–1505 (2018).
[Crossref]

S. J. Erickson-Bhatt, K. J. Mesa, M. Marjanovic, E. J. Chaney, A. Ahmad, P.-C. Huang, Z. G. Liu, K. Cunningham, and S. A. Boppart, “Intraoperative optical coherence tomography of the human thyroid: Feasibility for surgical assessment,” Transl. Res. 195, 13–24 (2018).
[Crossref]

R. Ladurner, N. Al Arabi, U. Guendogar, K. Hallfeldt, H. Stepp, and J. Gallwas, “Near-infrared autofluorescence imaging to detect parathyroid glands in thyroid surgery,” The Annals The Royal Coll. Surg. Engl. 100(1), 33–36 (2018).
[Crossref]

P. N. Taylor, D. Albrecht, A. Scholz, G. Gutierrez-Buey, J. H. Lazarus, C. M. Dayan, and O. E. Okosieme, “Global epidemiology of hyperthyroidism and hypothyroidism,” Nat. Rev. Endocrinol. 14(5), 301–316 (2018).
[Crossref]

2017 (6)

2016 (5)

Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomput. 187, 27–48 (2016).
[Crossref]

O. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6(1), 31689 (2016).
[Crossref]

O. M. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, M. I. Seider, J. A. Izatt, and C. A. Toth, “Optical coherence tomography for retinal surgery: perioperative analysis to real-time four-dimensional image-guided surgery,” Invest. Ophthalmol. Vis. Sci. 57(9), OCT37–OCT50 (2016).
[Crossref]

A. Uji, T. Murakami, S. Arichika, Y. Muraoka, S. Yoshitake, Y. Dodo, and N. Yoshimura, “Enhanced-resolution optical coherence tomography imaging,” Ophthalmologica 235(3), 163–172 (2016).
[Crossref]

J. Lee, S. Yi, Y. E. Kang, H.-W. Kim, K. H. Joung, H. J. Sul, K. S. Kim, and M. Shong, “Morphological and functional changes in the thyroid follicles of the aged murine and humans,” J. Pathol. Transl. Med. 50(6), 426–435 (2016).
[Crossref]

2015 (6)

J. Ferlay, I. Soerjomataram, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D. M. Parkin, D. Forman, and F. Bray, “Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012,” Int. J. Cancer 136(5), E359–E386 (2015).
[Crossref]

Q. R. Tummers, A. Schepers, J. F. Hamming, J. Kievit, J. V. Frangioni, C. J. van de Velde, and A. L. Vahrmeijer, “Intraoperative guidance in parathyroid surgery using near-infrared fluorescence imaging and low-dose methylene blue,” Surgery 158(5), 1323–1330 (2015).
[Crossref]

S. Sommerey, N. Al Arabi, R. Ladurner, C. Chiapponi, H. Stepp, K. K. Hallfeldt, and J. K. Gallwas, “Intraoperative optical coherence tomography imaging to identify parathyroid glands,” Surgical Endosc. 29(9), 2698–2704 (2015).
[Crossref]

A. A. Taha and A. Hanbury, “Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool,” BMC Med. Imaging 15(1), 29 (2015).
[Crossref]

S. Sommerey, R. Ladurner, N. Al Arabi, U. Mortensen, K. Hallfeldt, and J. Gallwas, “Backscattering intensity measurements in optical coherence tomography as a method to identify parathyroid glands,” Lasers Surg. Med. 47(6), 526–532 (2015).
[Crossref]

O. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, C. Chukwurah, P. Hahn, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Real-time 4d stereoscopic visualization of human ophthalmic surgery with swept-source microscope integrated optical coherence tomography,” Investig. Ophthalmol. & Vis. Sci. 56, 4085 (2015).
[Crossref]

2014 (1)

M. A. McWade, C. Paras, L. M. White, J. E. Phay, C. C. Solórzano, J. T. Broome, and A. Mahadevan-Jansen, “Label-free intraoperative parathyroid localization with near-infrared autofluorescence imaging,” The J. Clin. Endocrinol. & Metab. 99(12), 4574–4580 (2014).
[Crossref]

2013 (5)

R. Ladurner, K. K. Hallfeldt, N. Al Arabi, H. Stepp, S. Mueller, and J. K. Gallwas, “Optical coherence tomography as a method to identify parathyroid glands,” Lasers Surg. Med. 45(10), 654–659 (2013).
[Crossref]

L. C. Conti de Freitas, E. Phelan, L. Liu, J. Gardecki, E. Namati, W. C. Warger, G. J. Tearney, and G. W. Randolph, “Optical coherence tomography imaging during thyroid and parathyroid surgery: a novel system of tissue identification and differentiation to obviate tissue resection and frozen section,” Head Neck 36, 1329 (2013).
[Crossref]

G. Pellegriti, F. Frasca, C. Regalbuto, S. Squatrito, and R. Vigneri, “Worldwide increasing incidence of thyroid cancer: update on epidemiology and risk factors,” J. Cancer Epidemiol. 2013, 1–10 (2013).
[Crossref]

S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013).
[Crossref]

E. Garcia-Martin, L. E. Pablo, R. Herrero, J. R. Ara, J. Martin, J. M. Larrosa, V. Polo, J. Garcia-Feijoo, and J. Fernandez, “Neural networks to identify multiple sclerosis with optical coherence tomography,” Acta Ophthalmol. 91(8), e628–e634 (2013).
[Crossref]

2012 (1)

S. Ishida and N. Nishizawa, “Ex-vivo imaging of thyroid gland using ultrahigh-resolution optical coherence tomography at wavelength from 800 to 1700 nm,” Jpn. J. Appl. Phys. 51, 030203 (2012).
[Crossref]

2011 (2)

D. Lorenser, X. Yang, R. Kirk, B. Quirk, R. McLaughlin, and D. Sampson, “Ultrathin side-viewing needle probe for optical coherence tomography,” Opt. Lett. 36(19), 3894–3896 (2011).
[Crossref]

C. Paras, M. Keller, A. Mahadevan-Jansen, L. White, and J. Phay, “Near-infrared autofluorescence for the detection of parathyroid glands,” J. Biomed. Opt. 16(6), 067012 (2011).
[Crossref]

2010 (3)

R. L. Prosst, J. Weiss, L. Hupp, F. Willeke, and S. Post, “Fluorescence-guided minimally invasive parathyroidectomy: clinical experience with a novel intraoperative detection technique for parathyroid glands,” World J. Surg. 34(9), 2217–2222 (2010).
[Crossref]

C. Zhou, Y. Wang, A. D. Aguirre, T.-H. Tsai, D. W. Cohen, J. L. Connolly, and J. G. Fujimoto, “Ex vivo imaging of human thyroid pathology using integrated optical coherence tomography and optical coherence microscopy,” J. Biomed. Opt. 15(1), 016001 (2010).
[Crossref]

J. Probst, D. Hillmann, E. M. Lankenau, C. Winter, S. Oelckers, P. Koch, and G. Hüttmann, “Optical coherence tomography with online visualization of more than seven rendered volumes per second,” J. Biomed. Opt. 15(2), 026014 (2010).
[Crossref]

2008 (2)

X. Zhu, Y. Liang, Y. Mao, Y. Jia, Y. Liu, and G. Mu, “Analyses and calculations of noise in optical coherence tomography systems,” Front. Optoelectron. China 1(3-4), 247–257 (2008).
[Crossref]

A. Bergenfelz, S. Jansson, A. Kristoffersson, H. Märtensson, E. Reihnér, G. Wallin, and I. Lausen, “Complications to thyroid surgery: results as reported in a database from a multicenter audit comprising 3,660 patients,” Langenbeck’s Arch. Surg. 393(5), 667–673 (2008).
[Crossref]

2007 (2)

S.-W. Huang, A. D. Aguirre, R. A. Huber, D. C. Adler, and J. G. Fujimoto, “Swept source optical coherence microscopy using a fourier domain mode-locked laser,” Opt. Express 15(10), 6210–6217 (2007).
[Crossref]

A. M. Zysk, F. T. Nguyen, A. L. Oldenburg, D. L. Marks, and S. A. Boppart, “Optical coherence tomography: a review of clinical development from bench to bedside,” J. Biomed. Opt. 12(5), 051403 (2007).
[Crossref]

2004 (1)

1999 (1)

J. M. Schmitt, S. Xiang, and K. M. Yung, “Speckle in optical coherence tomography,” J. Biomed. Opt. 4(1), 95–106 (1999).
[Crossref]

1995 (1)

1979 (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979).
[Crossref]

Abdolmanafi, A.

Adler, D. C.

Aguirre, A. D.

C. Zhou, Y. Wang, A. D. Aguirre, T.-H. Tsai, D. W. Cohen, J. L. Connolly, and J. G. Fujimoto, “Ex vivo imaging of human thyroid pathology using integrated optical coherence tomography and optical coherence microscopy,” J. Biomed. Opt. 15(1), 016001 (2010).
[Crossref]

S.-W. Huang, A. D. Aguirre, R. A. Huber, D. C. Adler, and J. G. Fujimoto, “Swept source optical coherence microscopy using a fourier domain mode-locked laser,” Opt. Express 15(10), 6210–6217 (2007).
[Crossref]

Ahmad, A.

S. J. Erickson-Bhatt, K. J. Mesa, M. Marjanovic, E. J. Chaney, A. Ahmad, P.-C. Huang, Z. G. Liu, K. Cunningham, and S. A. Boppart, “Intraoperative optical coherence tomography of the human thyroid: Feasibility for surgical assessment,” Transl. Res. 195, 13–24 (2018).
[Crossref]

Al Arabi, N.

R. Ladurner, M. Lerchenberger, N. Al Arabi, J. K. Gallwas, H. Stepp, and K. K. Hallfeldt, “Parathyroid autofluorescence—how does it affect parathyroid and thyroid surgery? a 5 year experience,” Molecules 24(14), 2560 (2019).
[Crossref]

R. Ladurner, N. Al Arabi, U. Guendogar, K. Hallfeldt, H. Stepp, and J. Gallwas, “Near-infrared autofluorescence imaging to detect parathyroid glands in thyroid surgery,” The Annals The Royal Coll. Surg. Engl. 100(1), 33–36 (2018).
[Crossref]

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Figures (8)

Fig. 1.
Fig. 1. Data analysis steps. The OCT volumetric data is processed slice-by-slice to obtain a binarized version of the en face images. These are then stacked together forming a rough segmentation, which is improved by excluding the 3D segmented regions that do not resemble follicles based on a shape, border and depth score. Morphological analysis is then performed on the improved 3D segmentation to quantify the follicular density, count, size variability and sphericity of each ROI.
Fig. 2.
Fig. 2. 3D segmentation and shape score (S$_s$) concept visualization. (Left) sectional view of OCT volume with the 3D follicular segmentation obtained with the proposed method superimposed in orange. For visualization purposes, only 50 of the 283 segmented regions are visualized here. (Right) pictorial representation of the DICE-based sphericity measurement used as S$_s$ for each region. TP voxels inside the sphere are colored in light orange and FP voxels are illustrated in dark orange. FN voxels, not colored here, identify the inner sphere volume not belonging to the region. In this example the computed S$_s$ is 0.63.
Fig. 3.
Fig. 3. Representative OCT B-scan images of the thyroid tissue. The follicle diameters range between 30 and 550 µm. The variability in the intrafollicular signal intensity reflects the changes in colloid density (red arrows in a and c). Solid colloids appear as white spots in the intrafollicular space (red arrow in b). The imaging depth varies among and within samples, with dense follicular structures allowing for a larger imaging depth. Scale bars measure 400 µm in all the images. The FOV is [2.2$\times$1.8] mm, [1.4$\times$1.4] mm and [3.0$\times$1.8] mm for a, b and c, respectively.
Fig. 4.
Fig. 4. Comparison between OCT and histopathology images. Gray-scale images show OCT B-scans from different pathologies and normal samples. The corresponding histology images are shown below in H&E staining. (a) and (b) normal tissue, (c) papillary cancer, (d) adenoma and (e-h) goiter. F denotes follicles in the images. Scale bars are 500 µm in all images. FOV [1.4$\times$1.4] mm for d and h, and [1.8$\times$1.4] mm for the remaining.
Fig. 5.
Fig. 5. Evaluation of image smoothing after DWT filtering. In the top row, (left) the raw OCT image, (center) the filtered image with filter threshold 1.5 and (right) the filtered image with threshold 4. The second row presents the edge profile along the green line in the above images for filter threshold value of 1.5 and 4. In both graphs, the edge profile of the raw image (dotted green), filtered image (dashed blue) and enhanced image (solid red) are presented.
Fig. 6.
Fig. 6. Intermediate results of the 3D follicular segmentation. (a) Raw en face image along (b) the DWT filter version with filter threshold 1.5. (c) This latter was then contrast-enhanced and binarized using an intensity threshold. (d) Shape-score S$_s$ and (e) border-score S$_b$ of the 3D segmented regions were evaluated and combined, along with the depth-score S$_d$ not shown here, in S$_f$ (f) used to discriminate between follicular-like and noise-like regions, resulting in the final follicular segmentation (g). The red arrows in the binarized image (c) indicate points where regions were connected and later separated using the region separation procedure. (h) The 3D segmented follicular structure is presented with the segmented follicles in orange superimposed to the raw OCT data. Only the segmented follicles intersecting the 2D sectional view (a) are included.
Fig. 7.
Fig. 7. Automatic segmentation performance. Top row, en face segmentation validation images with the overimposed color-coded comparison between manual and automatic segmentation. Green, red and blue pixels identify TP, FP and FN respectively. Bottom row, OCT B-scans showing the follicular structure deep in the sample along the yellow line in the corresponding top en face images. The yellow lines in the B-scans mark the depth of the corresponding top en face image. The red arrows in (b) indicate follicles that present solid colloids. The DICE scores for the four examples are 0.93 (a), 0.82 (b), 0.86 (c) and 0.43 (d). Scale bars are 500 µm in all images.
Fig. 8.
Fig. 8. Follicular morphology analysis results for normal and thyroid pathologies. (a) Follicular density, (b) follicular volume variability, (c) number of follicles per mm3 and (d) follicular mean sphericity. The median values and ranges for the different pathologies and follicular morphology parameters are summarized in Table 1.

Tables (1)

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Table 1. Follicular morphology analysis results for normal and pathologic samples. For all the parameters, median and range are given.

Equations (3)

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2D  S p = 0.80 circularity + 0.25 solidity + 0.35 extent 0.40 eccentricity
S b = n S A n I + n T T + n L L + n B B
S f = S b + S d S s

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