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.

for intravascular oct (iVoct) images, we developed an automated atherosclerotic plaque characterization method that used a hybrid learning approach, which combined deep-learning convolutional and hand-crafted, lumen morphological features. processing was done on innate A-line units with labels fibrolipidic (fibrous tissue followed by lipidous tissue), fibrocalcific (fibrous tissue followed by calcification), or other. We trained/tested on an expansive data set (6,556 images), and performed an active learning, relabeling step to improve noisy ground truth labels. conditional random field was an important post-processing step to reduce classification errors. Sensitivities/ specificities were 84.8%/97.8% and 91.4%/95.7% for fibrolipidic and fibrocalcific plaques, respectively. Over lesions, en face classification maps showed automated results that agreed favorably to manually labeled counterparts. Adding lumen morphological features gave statistically significant improvement (p < 0.05), as compared to classification with convolutional features alone. Automated assessments of clinically relevant plaque attributes (arc angle and length), compared favorably to those from manual labels. our hybrid approach gave statistically improved results as compared to previous A-line classification methods using deep learning or hand-crafted features alone. This plaque characterization approach is fully automated, robust, and promising for live-time treatment planning and research applications. Intravascular optical coherence tomography (IVOCT) is an important technology for planning and assessment of interventional, percutaneous treatments of coronary artery disease. IVOCT is a high contrast, high-resolution imaging modality that uses near-infrared light 1. Compared to intravascular ultrasound (IVUS), this modality provides better image resolution with axial resolution ranging from 12 to 18 μm (as compared to 150-250 μm from IVUS) and lateral resolution ranging from 20 to 90 μm (as compared to 150-300 μm from IVUS) 1. IVOCT allows to determine different plaque components such as fibrous, lipidous, and calcified tissues, and is the only modality that can identify thin cap fibroatheroma 2. IVOCT is used for clinical, live-time intervention planning, and stent deployment assessment. IVOCT-guided percutaneous coronary intervention (PCI) brings valuable benefit for patient treatment as compared to PCI guided by X-ray angiography alone 3. In addition, IVOCT is used for clinical research studies such as the calcium scoring analysis 4 and calcium crack formation 5. Although IVOCT is clearly an excellent method for intravascular imaging of plaque, it has limitations. One is the cost of transducers. Another is tissue penetration depth, especially in the presence of lipidous plaque. Another is the need for a physician trained in visual interpretation of IVOCT images who is willing to take the time to examine images during a stressful procedure. A single IVOCT pullback typically generates 300-500 image frames resulting in data overload. Even ...

Major calcifications are of great concern when performing percutaneous coronary interventions because they inhibit proper stent deployment. We created a comprehensive software to segment calcifications in intravascular optical coherence tomography (IVOCT) images and to calculate their impact using the stentdeployment calcification score, as reported by Fujino et al. We segmented the vascular lumen and calcifications using the pretrained SegNet, convolutional neural network, which was refined for our task. We cleaned segmentation results using conditional random field processing. We evaluated the method on manually annotated IVOCT volumes of interest (VOIs) without lesions and with calcifications, lipidous, or mixed lesions. The dataset included 48 VOIs taken from 34 clinical pullbacks, giving a total of 2640 in vivo images. Annotations were determined from consensus between two expert analysts. Keeping VOIs intact, we performed 10-fold cross-validation over all data. Following segmentation noise cleaning, we obtained sensitivities of 0.85 AE 0.04, 0.99 AE 0.01, and 0.97 AE 0.01 for calcified, lumen, and other tissue classes, respectively. From segmented regions, we automatically determined calcification depth, angle, and thickness attributes. Bland-Altman analysis suggested strong correlation between manually and automatically obtained lumen and calcification attributes. Agreement between manually and automatically obtained stent-deployment calcification scores was good (four of five lesions gave exact agreement). Results are encouraging and suggest our classification approach could be applied clinically for assessment and treatment planning of coronary calcification lesions.

We developed a fully automated, two-step deep learning approach for characterizing coronary calcified plaque in intravascular optical coherence tomography (IVOCT) images. First, major calcification lesions were detected from an entire pullback using a 3D convolutional neural network (CNN). Second, a SegNet deep learning model with the Tversky loss function was used to segment calcified plaques in the major calcification lesions. The fully connected conditional random field and the frame interpolation of the missing calcification frames were used to reduce classification errors. We trained/tested the networks on a large dataset comprising 8,231 clinical images from 68 patients with 68 vessels and 4,320 ex vivo cadaveric images from 4 hearts with 4 vessels. The 3D CNN model detected major calcifications with high sensitivity (97.7%), specificity (87.7%), and F1 score (0.922). Compared to the standard one-step approach, our two-step deep learning approach significantly improved sensitivity (from 77.5% to 86.2%), precision (from 73.5% to 75.8%), and F1 score (from 0.749 to 0.781). We investigated segmentation performance for varying numbers of training samples; at least 3,900 images were required to obtain stable segmentation results. We also found very small differences in calcification attributes (e.g., angle, thickness, and depth) and identical calcium scores on repetitive pullbacks, indicating excellent reproducibility. Applied to new clinical pullbacks, our method has implications for real-time treatment planning and imaging research.

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