Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

Juhwan Lee; David Prabhu; Chaitanya Kolluru; Yazan Gharaibeh; Vladislav N. Zimin; Hiram G. Bezerra; David L. Wilson; David L. Wilson

doi:10.1364/BOE.10.006497

Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images

Juhwan Lee, David Prabhu, Chaitanya Kolluru, Yazan Gharaibeh, Vladislav N. Zimin, Hiram G. Bezerra, and David L. Wilson

Biomedical Optics Express
Vol. 10,
Issue 12,
pp. 6497-6515
(2019)
•https://doi.org/10.1364/BOE.10.006497

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Juhwan Lee, David Prabhu, Chaitanya Kolluru, Yazan Gharaibeh, Vladislav N. Zimin, Hiram G. Bezerra, and David L. Wilson, "Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images," Biomed. Opt. Express 10, 6497-6515 (2019)

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Table of Contents Category
- Optical Coherence Tomography
Optics & Photonics Topics
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The topics in this list come from the OSA Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: October 11, 2019
- Revised Manuscript: November 9, 2019
- Manuscript Accepted: November 10, 2019
- Published: November 25, 2019

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Open Access

Abstract

Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful for treatment planning and research, compared favorably (<4%) with those derived from ground-truth labels. When we converted the results to A-line classification, they were significantly better (p < 0.05) than those obtained previously by using deep learning classifications of A-lines.

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2019 (3)

D. Prabhu, H. Bezerra, C. Kolluru, Y. Gharaibeh, E. Mehanna, H. Wu, and D. Wilson, “Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets,” J. Biomed. Opt. 24(10), 1–15 (2019).
[Crossref]

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[Crossref]

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[Crossref]

2018 (5)

F. Liu, Z. Zhou, H. Jang, A. Samsonov, G. Zhao, and R. Kijowski, “Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging,” Magn. Reson. Med 79(4), 2379–2391 (2018).
[Crossref]

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A. Fujino, G. S. Mintz, M. Matsumura, T. Lee, S.-Y. Kim, M. Hoshino, E. Usui, T. Yonetsu, E. S. Haag, R. A. Shlofmitz, T. Kakuta, and A. Maehara, “A new optical coherence tomography-based calcium scoring system to predict stent underexpansion,” EuroIntervention 13(18), 2182–2189 (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]

C. Kolluru, D. Prabhu, Y. Gharaibeh, H. Bezerra, G. Guagliumi, and D. Wilson, “Deep neural networks for A-line-based plaque classification in coronary intravascular optical coherence tomography images,” J. Med. Imag. 5(04), 1 (2018).
[Crossref]

2017 (4)

Y. L. Yong, L. K. Tan, R. A. McLaughlin, K. H. Chee, and Y. M. Liew, “Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography,” J. Biomed. Opt. 22(12), 1–9 (2017).
[Crossref]

A. Abdolmanafi, L. Duong, N. Dahdah, and F. Cheriet, “Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography,” Biomed. Opt. Express 8(2), 1203–1220 (2017).
[Crossref]

K. Men, X. Chen, Y. Zhang, T. Zhang, J. Dai, J. Yi, and Y. Li, “Deep Deconvolutional Neural Network for Target Segmentation of Nasopharyngeal Cancer in Planning Computed Tomography Images,” Front. Oncol. 7, 315 (2017).
[Crossref]

V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” EEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017).
[Crossref]

2016 (3)

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]

D. Prabhu, E. Mehanna, M. Gargesha, E. Brandt, D. Wen, N. S. van Ditzhuijzen, D. Chamie, H. Yamamoto, Y. Fujino, A. Alian, J. Patel, M. Costa, H. G. Bezerra, and D. L. Wilson, “Three-dimensional registration of intravascular optical coherence tomography and cryo-image volumes for microscopic-resolution validation,” J. Med. Imag 3(2), 026004 (2016).
[Crossref]

N. Maejima, K. Hibi, K. Saka, E. Akiyama, M. Konishi, M. Endo, N. Iwahashi, K. Tsukahara, M. Kosuge, T. Ebina, S. Umemura, and K. Kimura, “Relationship Between Thickness of Calcium on Optical Coherence Tomography and Crack Formation After Balloon Dilatation in Calcified Plaque Requiring Rotational Atherectomy,” Circ. J. 80(6), 1413–1419 (2016).
[Crossref]

2015 (2)

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” Int J Comput Vis 115(3), 211–252 (2015).
[Crossref]

M. Gargesha, R. Shalev, D. Prabhu, K. Tanaka, A. M. Rollins, M. Costa, H. G. Bezerra, and D. L. Wilson, “Parameter estimation of atherosclerotic tissue optical properties from three-dimensional intravascular optical coherence tomography,” J. Med. Imag 2(1), 016001 (2015).
[Crossref]

2013 (1)

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), 1014–1030 (2013).
[Crossref]

2012 (3)

D. L. Hoyert and J. Xu, “Deaths: preliminary data for 2011,” Natl. Vital. Stat. Rep. 61(6), 1–51 (2012).

H. Lu, M. Gargesha, Z. Wang, D. Chamie, G. F. Attizzani, T. Kanaya, S. Ray, M. A. Costa, A. M. Rollins, H. G. Bezerra, and D. L. Wilson, “Automatic stent detection in intravascular OCT images using bagged decision trees,” Biomed. Opt. Express 3(11), 2809–2824 (2012).
[Crossref]

G. J. Tearney, E. Regar, T. Akasaka, T. Adriaenssens, P. Barlis, H. G. Bezerra, B. Bouma, N. Bruining, J. Cho, S. Chowdhary, M. A. Costa, R. de Silva, J. Dijkstra, C. Di Mario, D. Dudek, D. Dudeck, E. Falk, E. Falk, M. D. Feldman, P. Fitzgerald, H. M. Garcia-Garcia, H. Garcia, N. Gonzalo, J. F. Granada, G. Guagliumi, N. R. Holm, Y. Honda, F. Ikeno, M. Kawasaki, J. Kochman, L. Koltowski, T. Kubo, T. Kume, H. Kyono, C. C. S. Lam, G. Lamouche, D. P. Lee, M. B. Leon, A. Maehara, O. Manfrini, G. S. Mintz, K. Mizuno, M. Morel, S. Nadkarni, H. Okura, H. Otake, A. Pietrasik, F. Prati, L. Räber, M. D. Radu, J. Rieber, M. Riga, A. Rollins, M. Rosenberg, V. Sirbu, P. W. J. C. Serruys, K. Shimada, T. Shinke, J. Shite, E. Siegel, S. Sonoda, S. Sonada, M. Suter, S. Takarada, A. Tanaka, M. Terashima, T. Thim, T. Troels, S. Uemura, G. J. Ughi, H. M. M. van Beusekom, A. F. W. van der Steen, G.-A. van Es, G.-A. van Es, G. van Soest, R. Virmani, S. Waxman, N. J. Weissman, and G. Weisz, “and International Working Group for Intravascular Optical Coherence Tomography (IWG-IVOCT), “Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the International Working Group for Intravascular Optical Coherence Tomography Standardization and Validation,” J. Am. Coll. Cardiol. 59(12), 1058–1072 (2012).
[Crossref]

2010 (2)

G. van Soest, T. Goderie, E. Regar, S. Koljenović, G. L. J. H. van Leenders, N. Gonzalo, S. van Noorden, T. Okamura, B. E. Bouma, G. J. Tearney, J. W. Oosterhuis, P. W. Serruys, and A. F. W. van der Steen, “Atherosclerotic tissue characterization in vivo by optical coherence tomography attenuation imaging,” J. Biomed. Opt. 15(1), 011105 (2010).
[Crossref]

Z. Wang, H. Kyono, H. G. Bezerra, H. Wang, M. Gargesha, C. Alraies, C. Xu, J. M. Schmitt, D. L. Wilson, M. A. Costa, and A. M. Rollins, “Semiautomatic segmentation and quantification of calcified plaques in intracoronary optical coherence tomography images,” J. Biomed. Opt. 15(6), 061711 (2010).
[Crossref]

2009 (3)

E. Galkina and K. Ley, “Immune and Inflammatory Mechanisms of Atherosclerosis,” Annu. Rev. Immunol. 27(1), 165–197 (2009).
[Crossref]

H. G. Bezerra, M. A. Costa, G. Guagliumi, A. M. Rollins, and D. I. Simon, “Intracoronary optical coherence tomography: a comprehensive review clinical and research applications,” JACC: Cardiovascular Interventions 2(11), 1035–1046 (2009).
[Crossref]

J. Shotton, J. Winn, C. Rother, and A. Criminisi, “TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context,” Int J Comput Vis 81(1), 2–23 (2009).
[Crossref]

2008 (1)

C. Xu, J. M. Schmitt, S. G. Carlier, and R. Virmani, “Characterization of atherosclerosis plaques by measuring both backscattering and attenuation coefficients in optical coherence tomography,” J. Biomed. Opt. 13(3), 034003 (2008).
[Crossref]

2005 (1)

P.-T. de Boer, D. P. Kroese, S. Mannor, and R. Y. Rubinstein, “A Tutorial on the Cross-Entropy Method,” Ann Oper Res 134(1), 19–67 (2005).
[Crossref]

1945 (1)

L. R. Dice, “Measures of the Amount of Ecologic Association Between Species,” Ecology 26(3), 297–302 (1945).
[Crossref]

1912 (1)

P. Jaccard, “The Distribution of the Flora in the Alpine Zone.1,” New Phytol. 11(2), 37–50 (1912).
[Crossref]

Abdolmanafi, A.

A. Abdolmanafi, F. Cheriet, L. Duong, R. Ibrahim, and N. Dahdah, “An automatic diagnostic system of coronary artery lesions in Kawasaki disease using intravascular optical coherence tomography imaging,” J. Biophotonicse201900112 (2019).

Adam, H.

L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation,” in Computer Vision – ECCV 2018, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, eds., Lecture Notes in Computer Science (Springer International Publishing, 2018), pp. 833–851.

Adib, I. R.

Adriaenssens, T.

Akasaka, T.

Akiyama, E.

Alian, A.

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Fig. 1. SegNet architecture. The SegNet classifier comprises five encoding and five decoding steps. Each encoder includes 3 × 3 convolution, batch normalization, and rectified linear unit (ReLU) structures, followed by max-pooling, whereas max-unpooling is applied followed by convolution in the decoding step. The left and right figures indicate the original IVOCT image and predicted label, respectively. The input and output images sizes are exactly the same. Actual processing is done in the (r, θ) view, but the (x, y) view is shown here.

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Fig. 2. Example annotations for IVOCT images in (x, y) (a) and (r, θ) (b) views. The left and right columns show IVOCT images and expert annotations, respectively, with labels (M: lumen, L: lipid, C: calcium, and O: other). Because lipids are highly absorbing, we do not know their actual depth and assigned this class a consistent thickness of 350 μm. Here and in other figures, IVOCT images are shown following log transformation for improved visualization.

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Fig. 3. Clinical plaque attributes include (a) angle and (b) depth. Panel (a) is in (x, y) whereas panel (b) is in (r, θ). Yellow, green, and black indicate lumen, lipidous plaque, and other, respectively. For better visualization, only the lipidous plaque is shown, and images were cropped from their original size.

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Fig. 4. Pre-processing results for a representative IVOCT image frame. Panels are: (a) original (r, θ) image showing lumen segmentation in red as obtained by dynamic programming, (b) removal of A-lines corresponding to the guidewire and its shadow, (c) pixel-shifted data, and (d) speckle noise reduction.

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Fig. 5. Comparison of classification results between Deeplab v3+ and SegNet mapped to (x,y) view for three images. Panels show: (a) ground truth, (b) results obtained using Deeplab v3+, and (c) results obtained using SegNet. Colors are green (lipid), red (calcium), and white (guidewire).

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Fig. 6. Classification results (SegNet) before and after CRF noise cleaning mapped to (x,y) view. Panels show: (a) ground truth, (b) results prior to CRF noise cleaning, and (c) results after CRF noise cleaning. Colors are green (lipid), red (calcium), and white (guidewire).

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Fig. 7. Effects of pre-processing steps on classification segmentation. Panels are: (a) ground truth, (b) no pre-processing, (c) noise reduction but no pixel shifting, (d) pixel shifting but no noise reduction, and (e) pixel shifting and noise reduction. Colors are green (lipid), red (calcium), and white (guidewire). In general, pixel shifting had a very significant effect on results.

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Fig. 8. A-line classification results obtained by processing pixel-wise results (Methods). Note that an A-line is labeled according to its predominant tissues, starting from the lumen (e.g., fibrocalcific indicates an A-line with fibrous tissue followed by calcification). Panels show: (a) results obtained using Deeplab v3+ and (b) results obtained using SegNet. For each figure, the inner ring is the ground-truth label, and the outer ring is the predicted result. Red, green, and blue indicate fibrocalcific, fibrolipidic, and other classes, respectively. White is the guidewire.

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Fig. 9. A-line classification results in en face (θ,z) view. Panels in columns are (left) ground truth, (center) results obtained using Deeplab v3+, and (right) results obtained using SegNet. Red, green, and blue indicate fibrocalcific, fibrolipidic, and other A-line classes, respectively. White is the guidewire. Results are from one test fold data, including five VOIs (479 image frames). Cardiologists scored lesion results by performing a clinical score assessment (CSA), with the following rating scale: 1: strongly disagree, 2: disagree, 3: unsure, 4: agree, and 5: strongly agree. All three cardiologists agreed that the automated results will not change clinical decision making.

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Fig. 10. En face (θ,z) view of A-line classification results on a held-out test sample including 600 IVOCT images with nine VOIs without any identified calcification or lipidous region. Panels show: (left) ground truth, (center) result obtained using Deeplab v3+, and (right) result obtained using SegNet. See Fig. 8 for details.

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Tables (7)

Table 1. Mean performance metrics over folds, including sensitivity, specificity, Dice, and Jaccard coefficients, between Deeplab v3+ and SegNet. With SegNet, the sensitivities of fibrolipidic and fibrocalcific plaques were significantly improved (*p < 0.05). For statistical analysis, the Wilcoxon signed-rank test was performed.

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Table 2. Mean performance metrics (SegNet) over folds, including sensitivity, specificity, Dice, and Jaccard coefficients, before and after CRF noise cleaning. With CRF noise cleaning, the overall metrics were slightly increased but statistically insignificant (p > 0.05). For statistical analysis, the Wilcoxon signed-rank test was performed.

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Table 3. Mean performance metrics measured on a held-out test sample without any identified calcification or lipidous regions, including sensitivity, Dice, and Jaccard coefficients between Deeplab v3+ and SegNet. A held-out data set was composed of 600 IVOCT images from nine VOIs. Results shows that both methods are highly suitable for discriminating non-plaques. CRF noise cleaning did not significantly improve results (p > 0.05). For statistical analysis, the Wilcoxon signed-rank test was performed.

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Table 4. Metrics assessed over folds with and without pre-processing (pixel shifting and noise reduction). We found that pixel shifting helped significantly improve classification performance. Statistically significant differences (p < 0.05) compared with no pre-processing are indicated by an asterisk (*). The Wilcoxon signed-rank test was performed. To improve comparisons, all folds were exactly the same for all instances. All metrics were obtained after CRF noise cleaning.

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Table 5. Mean performance metrics over folds, including sensitivity, specificity, Dice, and Jaccard coefficients measured from A-line-based classification between Deeplab v3+ and SegNet. With SegNet, the sensitivity of fibrolipidic class increased by nearly 16% (from 74.2% to 90.1%) relative to that for the Deeplab v3+, whereas fibrocalcific tissue yielded an improvement of approximately 12%. Statistically significant differences (p < 0.05) compared with each class of Deeplab v3+ are indicated by an asterisk (*).

View Table | View all tables in this article

Table 6. Mean clinical plaque attributes over folds, including arc angle and depth. Both networks gave values close to those derived manually; i.e., the difference values (mean arc angle, depth) of Deeplab v3+ were (13.5°, 0.03 mm) and (11.3°, 0.004 mm) for lipidic and calcific lesions, respectively, while SegNet showed the values of (8.6°, 0.03 mm) and (9.4°, 0.005 mm), likely within the range of clinical relevance. Metrics were obtained after CRF noise cleaning. Despite differences being small relative to clinical impact, a Student’s t-test rejected the null hypothesis of no difference (*p < 0.05 and **p < 0.001).

View Table | View all tables in this article

Table 7. Comparison of the proposed method (SegNet) to the previously reported A-line CNN-based approach [18]. In almost all cases, the new method outperformed, with particularly large improvements in sensitivity, Dice, and Jaccard. Using the Wilcoxon signed-rank test, we determined statistically significant differences (p < 0.05) between the two methods in many instances, as indicated by an asterisk (*).

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Equations (6)

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E (L) = \sum_{i} δ_{i} (L_{i}) + \sum_{i < j} δ_{i} (L_{i}, L_{j})

δ_{i} (L_{i}, L_{j}) = γ (L_{i}, L_{j}) \sum_{b = 1}^{B} W^{(b)} G^{(b)} (V_{i}, V_{j})

G (V_{i}, V_{j}) = [W_{1} e^{(- \frac{{| P_{i} - P_{j} |}^{2}}{2 σ_{α}^{2}} - \frac{{| C_{i} - C_{j} |}^{2}}{2 σ_{β}^{2}})}] + [W_{2} e^{(- \frac{{| P_{i} - P_{j} |}^{2}}{2 σ_{ρ}^{2}})}]

φ (v)_{j} = \frac{e^{v_{j}}}{\sum_{k = 1}^{K} e^{v_{k}}} for j = 1, \dots, K

DC (A, B) = \frac{2 \cdot | A \cap B |}{| A | + | B |}

JC (A, B) = \frac{| A \cap B |}{| A \cup B |} = \frac{| A \cap B |}{| A | + | B | - | A \cap B |}

Classes		Sensitivity (%)	Specificity (%)	Dice	Jaccard
Deeplab v3+	Fibrolipidic	82.3 ± 10.3	92.1 ± 3.6	0.799 ± 0.048	0.693 ± 0.056
	Fibrocalcific	77.7 ± 9.9	97.4 ± 1.3	0.742 ± 0.058	0.621 ± 0.067
	Other	89.7 ± 4.3	82.8 ± 6.5	0.903 ± 0.023	0.832 ± 0.035
SegNet	Fibrolipidic	87.4 ± 7.2*	89.5 ± 4.6	0.801 ± 0.044	0.690 ± 0.058
	Fibrocalcific	85.1 ± 7.2*	94.2 ± 2.4	0.734 ± 0.085	0.594 ± 0.064
	Other	87.2 ± 3.2	81.5 ± 7.1	0.908 ± 0.031	0.837 ± 0.052

Classes		Sensitivity (%)	Specificity (%)	Dice	Jaccard
Lipidous	before CRF	88.7 ± 7.2	88.3 ± 5.0	0.794 ± 0.045	0.681 ± 0.058
Lipidous	after CRF	87.4 ± 7.2	89.5 ± 4.6	0.801 ± 0.044	0.690 ± 0.058
Calcified	before CRF	85.8 ± 7.0	93.6 ± 2.8	0.726 ± 0.088	0.578 ± 0.066
Calcified	after CRF	85.1 ± 7.2	94.2 ± 2.4	0.734 ± 0.085	0.594 ± 0.064
Other	before CRF	86.7 ± 3.2	81.9 ± 6.9	0.905 ± 0.032	0.833 ± 0.053
Other	after CRF	87.2 ± 3.2	81.5 ± 7.1	0.908 ± 0.031	0.837 ± 0.052

Method	Sensitivity (%)	Dice	Jaccard
Deeplab v3+	99.4	0.997	0.994
SegNet	99.0	0.995	0.990

Classes		Sensitivity (%)	Specificity (%)	Dice	Jaccard
Lipidous	no pre-processing	85.4 ± 6.9	91.5 ± 2.2	0.131 ± 0.113	0.080 ± 0.074
	noise reduction,but no pixel-shifting	86.5 ± 11.4	90.5 ± 5.0	0.119 ± 0.095	0.071 ± 0.061
	pixel-shifting,but no noise reduction	87.1 ± 6.1*	94.3 ± 1.3	0.798 ± 0.054*	0.685 ± 0.066*
	pixel shifting and noise reduction	87.4 ± 7.2*	89.5 ± 4.6	0.801 ± 0.044*	0.690 ± 0.058*
Calcified	no pre-processing	40.2 ± 18.1	92.4 ± 2.5	0.207 ± 0.084	0.124 ± 0.057
	noise reduction,but no pixel-shifting	43.2 ± 27.2	95.5 ± 1.9	0.243 ± 0.125	0.150 ± 0.093
	pixel-shifting,but no noise reduction	79.7 ± 8.7*	94.1 ± 0.8	0.682 ± 0.087*	0.552 ± 0.102*
	pixel shifting and noise reduction	85.1 ± 7.2*	94.2 ± 2.4	0.734 ± 0.085*	0.594 ± 0.064*
Other	no pre-processing	85.8 ± 2.7	87.7 ± 5.9	0.922 ± 0.016	0.856 ± 0.027
	noise reduction,but no pixel-shifting	87.5 ± 3.9	83.7 ± 8.4	0.931 ± 0.022	0.872 ± 0.039
	pixel-shifting,but no noise reduction	86.9 ± 1.3	79.3 ± 8.6	0.900 ± 0.012*	0.824 ± 0.020
	pixel shifting and noise reduction	87.2 ± 3.2	81.5 ± 7.1	0.908 ± 0.031*	0.837 ± 0.052

Classes		Sensitivity (%)	Specificity (%)	Dice	Jaccard
Deeplab v3+	Fibrolipidic	74.2 ± 10.9	93.8 ± 4.4	0.780 ± 0.077	0.646 ± 0.099
	Fibrocalcific	81.1 ± 11.1	96.1 ± 3.9	0.818 ± 0.074	0.698 ± 0.103
	Other	91.1 ± 5.3	82.9 ± 6.1	0.888 ± 0.029	0.800 ± 0.047
SegNet	Fibrolipidic	90.1 ± 3.9*	84.3 ± 6.6*	0.827 ± 0.057*	0.733 ± 0.073*
	Fibrocalcific	92.9 ± 4.5*	76.4 ± 6.2*	0.897 ± 0.038*	0.827 ± 0.057*
	Other	87.9 ± 3.2	80.0 ± 6.9	0.907 ± 0.023	0.836 ± 0.036

	Lipidous Plaque		Calcified Plaque
	Arc Angle (°)*	Depth (mm)**	Arc Angle (°)**	Depth (mm)
Ground Truth	146.0 ± 44.1	0.151 ± 0.035	85.0 ± 35.5	0.050 ± 0.013
Deeplab v3+	157.5 ± 47.8*	0.121 ± 0.057**	93.0 ± 72.1*	0.048 ± 0.036
SegNet	152.3 ± 41.0*	0.121 ± 0.024**	88.6 ± 39.1**	0.049 ± 0.012

Classes		Sensitivity (%)	Specificity (%)	Dice	Jaccard
Fibrolipidic	Previous Method	69.3 ± 15.7	92.3 ± 4.3	0.737 ± 0.103	0.593 ± 0.124
Fibrolipidic	Current Method	90.1 ± 3.9*	84.3 ± 6.6*	0.827 ± 0.057*	0.733 ± 0.073*
Fibrocalcific	Previous Method	75.1 ± 16.2	94.4 ± 3.6	0.721 ± 0.120	0.576 ± 0.147
Fibrocalcific	Current Method	92.9 ± 4.5*	76.4 ± 6.2*	0.897 ± 0.038*	0.827 ± 0.057*
Other	Previous Method	89.2 ± 3.6	79.2 ± 7.4	0.866 ± 0.034	0.766 ± 0.053
Other	Current Method	87.9 ± 3.2	80.0 ± 6.9	0.907 ± 0.023*	0.836 ± 0.036*