Name
Abstract | ||
|---|---|---|
Accurate three-dimensional (3D) segmentation of the coronary artery lumen is an essential step in the quantitative analysis of coronary artery stenosis. However, due to the small size and complex structure of the coronary artery tree, it is difficult and laborious to perform voxel-by-voxel labeling of the lumen on 3D cardiac computed tomography angiography (CCTA) images. Since radiologists tend to focus only on the regions of interest, the annotations collected are often imperfect and contain some false-negative targets. To address the problem of partial annotations, a 3D convolutional neural network (CNN)-based approach for coronary artery lumen segmentation is proposed. Our CNN model adopts an U-Net like backbone and has multiple auxiliary branches on the expanding path. In the inference stage, an uncertain map of the network prediction is generated from the multiple abstract feature maps, which is used to refine the segmentation results. Experimental results on the MICCAI 2020 Automated Segmentation of Coronary Arteries (ASOCA) challenge dataset show that our method achieves even better segmentation accuracy with partial annotations than the backbone model trained with full annotations. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISBI48211.2021.9434025 | 2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
Keywords | DocType | ISSN |
Coronary artery lumen, semantic segmentation, partial annotations, 3D CNN, uncertainty estimation | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Chen | 1 | 0 | 0.34 |
| Chen Wei | 2 | 0 | 0.34 |
| Shenghan Ren | 3 | 0 | 0.34 |
| Zhen Zhou | 4 | 0 | 0.34 |
| Lei Xu | 5 | 0 | 0.34 |
| Jimin Liang | 6 | 0 | 0.34 |