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.

Abstract.We develop neural-network-based methods for classifying plaque types in clinical intravascular optical coherence tomography (IVOCT) images of coronary arteries. A single IVOCT pullback can consist of >500 microscopic-resolution images, creating both a challenge for physician interpretation during an interventional procedure and an opportunity for automated analysis. In the proposed method, we classify each A-line, a datum element that better captures physics and pathophysiology than a voxel, as a fibrous layer followed by calcification (fibrocalcific), a fibrous layer followed by a lipidous deposit (fibrolipidic), or other. For A-line classification, the usefulness of a convolutional neural network (CNN) is compared with that of a fully connected artificial neural network (ANN). A total of 4469 image frames across 48 pullbacks that are manually labeled using consensus labeling from two experts are used for training, evaluation, and testing. A 10-fold cross-validation using held-out pullbacks is applied to assess classifier performance. Noisy A-line classifications are cleaned by applying a conditional random field (CRF) and morphological processing to pullbacks in the en-face view. With CNN (ANN) approaches, we achieve an accuracy of 77.7%±4.1% (79.4%±2.9%) for fibrocalcific, 86.5%±2.3% (83.4%±2.6%) for fibrolipidic, and 85.3%±2.5% (82.4%±2.2%) for other, across all folds following CRF noise cleaning. The results without CRF cleaning are typically reduced by 10% to 15%. The enhanced performance of the CNN was likely due to spatial invariance of the convolution operation over the input A-line. The predicted en-face classification maps of entire pullbacks agree favorably to the annotated counterparts. In some instances, small error regions are actually hard to call when re-examined by human experts. Even in worst-case pullbacks, it can be argued that the results will not negatively impact usage by physicians, as there is a preponderance of correct calls.

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.

Background Coronary artery calcification (CAC) presents unique challenges for percutaneous coronary intervention. Calcium appears as a signal-poor region with well-defined borders by FD-OCT, which might enable full quantification of CAC. The objective of this study was to demonstrate the accuracy of intravascular frequency-domain optical coherence tomography (FD-OCT) to determine distribution of CAC. Methods and Results Cadaveric coronary arteries were imaged using FD-OCT at 100Dm frame interval. Arteries were subsequently frozen, sectioned and imaged in their entire length at 20Dm intervals using the Case Cryo-Imaging automated system™. Full volumetric co-registration between FD-OCT and cryo-images was performed. Calcium area, distance from lumen and angle were traced on every cross-section and volumetric quantification was performed offline using a dedicated algorithm. Thirty left anterior descending (LAD) arteries were imaged by both FD-OCT and cryo-imaging. Of these, 13 vessels had a total of 55 plaques with calcification by cryo-imaging and FD-OCT identified 47 (85%) of these plaques. Quantitative analyses of 1285 cryo-images were compared with corresponding co-registered 257 FD-OCT images. Calcium distribution, represented by the calcium-lumen distance (depth) and the mean calcium angle, was similar with excellent correlation between FD-OCT and cryo-imaging respectively (calcium-lumen distance: 0.25±0.09mm vs. 0.26±0.12mm, p=0.742; R=0.90), (mean calcium angle: 35.33±21.86° vs. 39.68±26.61°, p=0.207; R=0.88). Volumetric quantification of CAC was possible by OCT; calcium volume was underestimated in large calcifications in which the abluminal plaque border could not be well visualized (3.11±2.14mm3 vs. 4.58±3.39mm3, p=0.001) in OCT vs. cryo respectively. Conclusion Intravascular FD-OCT can accurately characterize CAC distribution. OCT can quantify absolute calcium volume, but may underestimate calcium burden in large plaques with poorly defined abluminal borders.