Zheng Sun, Shuyan Wang. Application of Deep Learning in Intravascular Optical Coherence Tomography[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2200002
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2200002 (2022)
![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](/richHtml/lop/2022/59/22/2200002/img_01.jpg)
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
![Illustration of SegNet architecture[20]](/richHtml/lop/2022/59/22/2200002/img_02.jpg)
Fig. 2. Illustration of SegNet architecture[20]
![Segmentation results of SegNet[22]. (a) Original image; (b) ground truth; (c) initial segmentation by SegNet; (d) output after CRF processing](/Images/icon/loading.gif){kind=icon}
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]
|
Table 1. Comparison of six DL-based methods for IVOCT image segmentation
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20