Name
Abstract | ||
|---|---|---|
In this paper, we aim to extract the aortic knuckle (AK) contour in chest radiographs, an anatomical structure rarely being addressed in the literature. Since the AK structure is small and thin, simply adopting the deep network methods that are successful for large organ segmentation is inadequate for achieving good pixel-level accuracy and resolving local ambiguities. To address this challenge, we propose a new coarse-to-fine segmentation approach which focuses on global and local information contexts, respectively. Two convolutional networks are used. For the coarse segmentation, we use FasterRCNN; for the fine segmentation, we use U-Net. Our evaluation uses the publicly available JSRT dataset; the results are promising. Besides presenting these results, we analyze issues such as the imprecision of manual contour marking, and automatic generation of the coarse segmentation ground-truth mask used for deep network training. Our approach is general and can be applied to extract other curve-like objects-of-interest. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8513560 | EMBC |
Field | DocType | Volume |
Computer vision,Segmentation,Computer science,Image segmentation,Artificial intelligence,Radiography,Knuckle,Deep learning | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyun Xue | 1 | 245 | 22.97 |
| L. Rodney Long | 2 | 534 | 56.98 |
| Stefan Jaeger | 3 | 147 | 10.72 |
| Les R Folio | 4 | 3 | 2.79 |
| George R. Thoma | 5 | 1207 | 132.81 |
| Sameer Antani | 6 | 1402 | 134.03 |