Name
Abstract | ||
|---|---|---|
Multi-organ segmentation is a critical problem in medical image analysis due to its great value for computer-aided diagnosis, computer-aided surgery, and radiation therapy. Although fully-supervised segmentation methods can achieve good performance, they usually require a large amount of 3D data, such as CT scans, with voxel-wised annotations which are usually difficult, expensive, and slow to obtain. By contrast, large unannotated datasets of CT images are available. Inspired by the well-known semi-supervised learning framework co-training, we propose multi-planar co-training (MPCT), to generate more reliable pseudo-labels by enforcing consistency among multiple planes, i.e., saggital, coronal, and axial planes, of 3D unlabeled medical data, which play a vital role in our framework. Empirical results show that generating pseudo-labels by the multi-planar fusion rather than a single plane leads to a significant performance gain. We evaluate our approach on a new collected dataset and show that MPCT boosts the performance of a typical segmentation model, fully convolutional networks, by a large margin, when only a small set of labeled 3D data is available, i.e., 77.49% vs. 73.14%. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Coronal plane,Pattern recognition,Segmentation,Computer science,Co-training,Planar,Artificial intelligence,Small set |
DocType | Volume | Citations |
Journal | abs/1804.02586 | 3 |
PageRank | References | Authors |
0.39 | 28 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyin Zhou | 1 | 97 | 10.94 |
| Yan Wang | 2 | 134 | 11.13 |
| Peng Tang | 3 | 80 | 11.47 |
| Wei Shen | 4 | 464 | 26.02 |
| Elliot K. Fishman | 5 | 164 | 27.51 |
| Alan L. Yuille | 6 | 10339 | 1902.01 |