Application of Deep Learning in Intravascular Optical Coherence Tomography

Zheng Sun; Shuyan Wang

doi:10.3788/LOP202259.2200002

[1] Otsuka K, Villiger M, Nadkarni S K et al. Intravascular polarimetry: clinical translation and future applications of catheter-based polarization sensitive optical frequency domain imaging[J]. Frontiers in Cardiovascular Medicine, 7, 146(2020).

[2] Hu F, Lin Y, Hou M D et al. Super-resolution reconstruction of cytoskeleton image based on deep learning[J]. Acta Optica Sinica, 40, 2410001(2020).

[3] Xiao K, Tian L J, Wang Z Y. Fast super-resolution fluorescence microscopy imaging with low signal-to-noise ratio based on deep learning[J]. Chinese Journal of Lasers, 47, 1007002(2020).

[4] Fedewa R, Puri R S, Fleischman E et al. Artificial intelligence in intracoronary imaging[J]. Current Cardiology Reports, 22, 46(2020).

[5] Litjens G, Ciompi F, Wolterink J M et al. State-of-the-art deep learning in cardiovascular image analysis[J]. JACC: Cardiovascular Imaging, 12, 1549-1565(2019).

[6] Bologna M, Migliori S, Montin E et al. Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: application to hemodynamics modeling[J]. PLoS One, 14, e0213603(2019).

[7] Dallalazar N, Ayatollahi A, Habibi M et al. Automatic vessel wall segmentation of IVOCT images using region detection EREL algorithm[C], 611-616(2019).

[8] Zhao H S, He B, Ding Z Y et al. Automatic lumen segmentation in intravascular optical coherence tomography using morphological features[J]. IEEE Access, 7, 88859-88869(2019).

[9] Habibi M, Ayatollahi A, Dallalazar N et al. Lumen boundary detection using neutrosophic c-means in IVOCT images[C], 599-604(2019).

[10] Olender M L, Athanasiou L S, de la Torre H J M et al. Simultaneous multi-surface fitting for vessel wall layer delineation[C](2019).

[11] Sun S H, Sonka M, Beichel R R. Graph-based IVUS segmentation with efficient computer-aided refinement[J]. IEEE Transactions on Medical Imaging, 32, 1536-1549(2013).

[12] Roy A G, Conjeti S, Carlier S G et al. Lumen segmentation in intravascular optical coherence tomography using backscattering tracked and initialized random walks[J]. IEEE Journal of Biomedical and Health Informatics, 20, 606-614(2016).

[13] Tsantis S, Kagadis G C, Katsanos K et al. Automatic vessel lumen segmentation and stent strut detection in intravascular optical coherence tomography[J]. Medical Physics, 39, 503-513(2012).

[14] Joseph S, Adnan A, Adlam D. Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method[J]. Journal of Medical Imaging, 3, 044001(2016).

[15] Wang P Y, Zhu H Q, Ling X F. Intravascular optical coherence tomography image segmentation based on Gaussian mixture model and adaptive fourth-order PDE[J]. Signal, Image and Video Processing, 14, 29-37(2020).

[16] Li D W, Wu J M, He Y F et al. Parallel deep neural networks for endoscopic OCT image segmentation[J]. Biomedical Optics Express, 10, 1126-1135(2019).

[17] Balaji A, Kelsey L J, Majeed K et al. Coronary artery segmentation from intravascular optical coherence tomography using deep capsules[EB/OL]. https://arxiv.org/abs/2003.06080

[18] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[EB/OL]. https://arxiv.org/abs/1505.04597

[19] Li L C, Jia T. Optical coherence tomography vulnerable plaque segmentation based on deep residual U-net[J]. Reviews in Cardiovascular Medicine, 20, 171-177(2019).

[20] Lee J, Gharaibeh Y, Kolluru C et al. Segmentation of coronary calcified plaque in intravascular OCT images using a two-step deep learning approach[J]. IEEE Access, 8, 225581-225593(2020).

[21] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[22] Gharaibeh Y, Prabhu D S, Kolluru C et al. Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring[J]. Journal of Medical Imaging, 6, 045002(2019).

[23] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[EB/OL]. https://arxiv.org/abs/1710.09829

[24] Tang J J, Lan Y S, Chen S R et al. Lumen contour segmentation in IVOCT based on N-type CNN[J]. IEEE Access, 7, 135573-135581(2019).

[25] Huang C X, Lan Y S, Xu G W et al. A deep segmentation network of multi-scale feature fusion based on attention mechanism for IVOCT lumen contour[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18, 62-69(2021).

[26] Gao Z F, Chung J, Abdelrazek M et al. Privileged modality distillation for vessel border detection in intracoronary imaging[J]. IEEE Transactions on Medical Imaging, 39, 1524-1534(2020).

[27] Prabhu D S, Bezerra H G, Kolluru C et al. Automated A-line coronary plaque classification of intravascular optical coherence tomography images using handcrafted features and large datasets[J]. Journal of Biomedical Optics, 24, 106002(2019).

[28] Kolluru C, Prabhu D, Gharaibeh Y et al. Voxel-based plaque classification in coronary intravascular optical coherence tomography images using decision trees[J]. Proceedings of SPIE, 10575, 105752Y(2018).

[29] Ughi G J, Adriaenssens T, Sinnaeve P et al. Automated tissue characterization of in vivo atherosclerotic plaques by intravascular optical coherence tomography images[J]. Biomedical Optics Express, 4, 1014-1030(2013).

[30] Zhou P, Zhu T J, He C L et al. Automatic classification of atherosclerotic tissue in intravascular optical coherence tomography images[J]. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 34, 1152-1159(2017).

[31] Athanasiou L S, Bourantas C V, Rigas G et al. Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images[J]. Journal of Biomedical Optics, 19, 026009(2014).

[32] Rico-Jimenez J J, Campos-Delgado D U, Villiger M et al. Automatic classification of atherosclerotic plaques imaged with intravascular OCT[J]. Biomedical Optics Express, 7, 4069-4085(2016).

[33] Gong P J, Almasian M, van Soest G et al. Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation[J]. Journal of Biomedical Optics, 25, 040901(2020).

[34] Boi A, Jamthikar A D, Saba L C et al. A survey on coronary atherosclerotic plaque tissue characterization in intravascular optical coherence tomography[J]. Current Atherosclerosis Reports, 20, 33(2018).

[35] Liu J, Ding N, Yu Y et al. Optimized depth-resolved estimation to measure optical attenuation coefficients from optical coherence tomography and its application in cerebral damage determination[J]. Journal of Biomedical Optics, 24, 035002(2019).

[36] Sun Z, Yang F, Sun J. Method for parametric imaging of attenuation by intravascular optical coherence tomography[J]. Biomedical Optics Express, 12, 1882-1904(2021).

[37] Gan Y, Tsay D, Amir S B et al. Automated classification of optical coherence tomography images of human atrial tissue[J]. Journal of Biomedical Optics, 21, 101407(2016).

[38] He C L, Li Z L, Wang J Q et al. Atherosclerotic plaque tissue characterization: an OCT-based machine learning algorithm with ex vivo validation[J]. Frontiers in Bioengineering and Biotechnology, 8, 749(2020).

[39] He S H, Zheng J, Maehara A et al. Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images[J]. Proceedings of SPIE, 10574, 1057432(2018).

[40] Litjens G, Kooi T, Bejnordi B E et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 42, 60-88(2017).

[41] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[42] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

[43] Gessert N, Lutz M, Heyder M et al. Automatic plaque detection in IVOCT pullbacks using convolutional neural networks[J]. IEEE Transactions on Medical Imaging, 38, 426-434(2019).

[44] Abdolmanafi A, Duong L, Dahdah N et al. Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography[J]. Biomedical Optics Express, 8, 1203-1220(2017).

[45] Gessert N, Heyder M, Latus et al. Plaque classification in coronary arteries from IVOCT images using convolutional neural networks and transfer learning[EB/OL]. https://arxiv.org/abs/1804.03904

[46] He C L, Yin Y F, Wang J Q et al. Automatic calcified plaques detection in the OCT pullbacks using convolutional neural networks[J]. Journal of Medical Biomechanics, 34, 109-110(2019).

[47] Liu X L, Du J X, Yang J L et al. Coronary artery fibrous plaque detection based on multi-scale convolutional neural networks[J]. Journal of Signal Processing Systems, 92, 325-333(2020).

[48] Cheimariotis G A, Riga M, Toutouzas K et al. Deep learning method to detect plaques in IVOCT images[J]. IFMBE Proceedings, 74, 389-395(2020).

[49] Xu M D, Cheng J, Li A N et al. Fibroatheroma identification in intravascular optical coherence tomography images using deep features[C], 1501-1504(2017).

[50] Abdolmanafi A, Duong L, Dahdah N et al. Characterization of coronary artery pathological formations from OCT imaging using deep learning[J]. Biomedical Optics Express, 9, 4936-4960(2018).

[51] Abdolmanafi A, Duong L, Ibrahim R et al. A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images[J]. Medical Physics, 48, 3511-3524(2021).

[52] Wei Y Z, Yuan X, Lan G P et al. Research progress and application of cardiovascular optical coherence tomography[J]. Laser & Optoelectronics Progress, 58, 2400001(2021).

[53] Lu Y F, Cao Y H, Jin Q H et al. Adaboost-based detection and segmentation of bioresorbable vascular scaffolds struts in IVOCT images[C], 4432-4436(2017).

[54] Cao Y H, Jin Q H, Lu Y F et al. Automatic analysis of bioresorbable vascular scaffolds in intravascular optical coherence tomography images[J]. Biomedical Optics Express, 9, 2495-2510(2018).

[55] Lu Y F, Jin Q H, Jing J et al. Detection and segmentation algorithm for bioresorbable vascular scaffolds struts based on machine learning[J]. Acta Optica Sinica, 38, 0215005(2018).

[56] Lu H, Lee J, Ray S et al. Automated stent coverage analysis in intravascular OCT (IVOCT) image volumes using a support vector machine and mesh growing[J]. Biomedical Optics Express, 10, 2809-2828(2019).

[57] Lu H, Lee J, Jakl M et al. Application and evaluation of highly automated software for comprehensive stent analysis in intravascular optical coherence tomography[J]. Scientific Reports, 10, 2150(2020).

[58] Qin X J, Yao L L, Jin Q H et al. Corner detection based automatic segmentation of bioresorbable vascular scaffold struts in IVOCT images[C], 604-607(2018).

[59] Zhou W, Chen F, Zong Y S et al. Automatic detection approach for bioresorbable vascular scaffolds using a U-shaped convolutional neural network[J]. IEEE Access, 7, 94424-94430(2019).

[60] Li L C, Jia T, Meng T Q et al. Deep convolutional neural networks for cardiovascular vulnerable plaque detection[J]. MATEC Web of Conferences, 277, 02024(2019).

[61] Jiang X L, Zeng Y Q, Xiao S X et al. Automatic detection of coronary metallic stent struts based on YOLOv3 and R-FCN[J]. Computational and Mathematical Methods in Medicine, 2020, 1793517(2020).

[62] Zhang H Q, Wang G L, Li Y et al. Faster R-CNN, fourth-order partial differential equation and global-local active contour model (FPDE-GLACM) for plaque segmentation in IV-OCT image[J]. Signal, Image and Video Processing, 14, 509-517(2020).

[63] Cao Y H, Lu Y F, Li J N et al. Deep learning based bioresorbable vascular scaffolds detection in IVOCT images[C], 3778-3783(2018).

[64] Cao Y H, Lu Y F, Jin Q H et al. Deep learning-based detection and segmentation for BVS struts in IVOCT images[C], 11043, 55-63(2018).

[65] Gessert N, Latus S, Abdelwahed Y S et al. Bioresorbable scaffold visualization in IVOCT images using CNNs and weakly supervised localization[J]. Proceedings of SPIE, 10949, 109492C(2019).

[66] Porto C D N, Filho C F F C, Macedo M M G et al. Classification of bifurcations regions in IVOCT images using support vector machine and artificial neural network models[J]. Proceedings of SPIE, 10134, 101344D(2017).

[67] Macedo M M G, Guimarães W V N, Galon M Z et al. A bifurcation identifier for IV-OCT using orthogonal least squares and supervised machine learning[J]. Computerized Medical Imaging and Graphics, 46, 237-248(2015).

[68] Wang A C, Eggermont J, Reiber J H C et al. Fully automated side branch detection in intravascular optical coherence tomography pullback runs[J]. Biomedical Optics Express, 5, 3160-3173(2014).

[69] Miyagawa M, Costa M G F, Gutierrez M A et al. Using convolutional neural networks for classification of bifurcation regions in IVOCT images[C], 2019, 5597-5600(2019).

[70] Miyagawa M, Costa M G F, Gutierrez M A et al. Detecting vascular bifurcation in IVOCT images using convolutional neural networks with transfer learning[J]. IEEE Access, 7, 66167-66175(2019).