Name
Abstract | ||
|---|---|---|
Recent advancements in medical image segmentation techniques have achieved compelling results. However, most of the widely used approaches do not take into account any prior knowledge about the shape of the biomedical structures being segmented. More recently, some works have presented approaches to incorporate shape information. However, many of them are indeed introducing more parameters to the segmentation network to learn the general features, which any segmentation network is able learn, instead of specifically shape features. In this paper, we present a novel approach that seamlessly integrates the shape information into the segmentation network. Experiments on human brain MRI segmentation demonstrate that our approach can achieve a lower Hausdorff distance and higher Dice coefficient than the state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00889-5_7 | DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018 |
Field | DocType | Volume |
Pattern recognition,Sørensen–Dice coefficient,Segmentation,Computer science,Image segmentation,Affine invariant,Hausdorff distance,Artificial intelligence | Conference | 11045 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou He | 1 | 15 | 4.62 |
| Siqi Bao | 2 | 10 | 3.22 |
| Albert C. S. Chung | 3 | 964 | 72.07 |