Name
Abstract | ||
|---|---|---|
This paper proposes a novel image segmentation approach that integrates fully convolutional networks (FCNs) with a level set model. Compared with a FCN, the integrated method can incorporate smoothing and prior information to achieve an accurate segmentation. Furthermore, different than using the level set model as a post-processing tool, we integrate it into the training phase to fine-tune the FCN. This allows the use of unlabeled data during training in a semi-supervised setting. Using two types of medical imaging data (liver CT and left ventricle MRI data), we show that the integrated method achieves good performance even when little training data is available, outperforming the FCN or the level set model alone. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67558-9_15 | DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT |
Keywords | DocType | Volume |
Image segmentation, Level set, Deep learning, FCN, Semi-supervised learning, Shape prior | Conference | 10553 |
ISSN | Citations | PageRank |
0302-9743 | 7 | 0.56 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Tang | 1 | 623 | 51.33 |
| Sepehr Valipour | 2 | 19 | 2.10 |
| Zichen Vincent Zhang | 3 | 9 | 2.26 |
| Dana Cobzas | 4 | 207 | 22.19 |
| Martin Jagersand | 5 | 100 | 10.96 |