Fig. 1. Original IVOCT image and comparison of segmentation results of five methods^[19]. (a) Original image; (b) manual segmentation by specialists; (c) segmentation by prototype U-Net; (d) segmentation by prototype U-Net with proposed loss function; (e) segmentation by deep Residual U-Net and ResNet101; (f) segmentation by deep Residual U-Net and ResNet101 with proposed loss function

Fig. 2. Illustration of SegNet architecture^[20]

Fig. 3. Segmentation results of SegNet^[22]. (a) Original image; (b) ground truth; (c) initial segmentation by SegNet; (d) output after CRF processing

Fig. 4. Segmentation results of IVOCT images by DeepCap^[17]

Fig. 5. Segmentation results of lumen contour in an IVOCT image with metal stent^[24].^{(a) Original image; (b) ground truth; (c) U-Net output; (d) N-Net output}

Fig. 6. Segmentation results of IVOCT lumen contour^[25].^{(a) Original images; (b) ground truth; (c) U-Net output; (d) FCN output; (e) SegNet output; (f) RSM-Network output}

Fig. 7. Comparison of segmentation results of vessel border from IVOCT images by different methods^[26]. (a) Vessel bifurcation; (b) white thrombus; (c) red thrombus; (d) complex thrombus

Fig. 8. Polar views of IVOCT images after tissue characterization where the patches with red represent plaque tissue and those with green are normal vessel wall^[48]. (a) Lipid plaque; (b) calcified and fibrous plaque; (c) mixed plaque; (d) calcified plaque

Fig. 9. Classification results for each coronary artery tissue type in polar views of IVOCT images^[50]. (a) Intima; (b) media; (c) fibrosis

Fig. 10. Detection result of metallic stents in IVOCT images^[61]. (a) YOLOv3; (b) R-FCN

Fig. 11. Results of stent detection in IVOCT images^[65]. (a) No stent; (b) metal stent; (c) BVS

Fig. 12. Flowchart of ML-based method for automatic identification of vascular bifurcations^[67]

Fig. 13. CNN architecture used to classify bifurcation regions in IVOCT images^[69]