Name
Abstract | ||
|---|---|---|
Segmentation of abdominal adipose tissues (AAT) into subcutaneous adipose tissues (SAT) and visceral adipose tissues (VAT) is of crucial interest for managing the obesity. Previous methods with raw or hand-crafted features rarely work well on large-scale subject cohorts, because of the inhomogeneous image intensities, artifacts and the diverse distributions of VAT. In this paper, we propose a novel two-stage coarse-to-fine algorithm for AAT segmentation. In the first stage, we formulate the AAT segmentation task as a pixel-wise classification problem. First, three types of features, intensity, spatial and contextual features, are extracted. Second, a new type of deep neural network, named multi-scale deep neural network (MSDNN), is provided to extract high-level features. In the second stage, to improve the segmentation accuracy, we refine coarse segmentation results by determining the internal boundaries of SAT based on coarse segmentation results and the continuous of SAT internal boundaries. Finally, we demonstrate the efficacy of our algorithm for both 2D and 3D cases on a wide population range. Compared with other algorithms, our method is not only more suitable for large-scale dataset, but also achieves better segmentation results. Furthermore, our system takes about 2s to segment an abdominal image, which implies potential clinical applications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2016.07.059 | Neurocomputing |
Keywords | Field | DocType |
Abdominal adipose tissues segmentation,Coarse-to-fine segmentation,Multi-scale deep neural network,Internal boundary | Population,Scale-space segmentation,Pattern recognition,Segmentation,Segmentation-based object categorization,Artificial intelligence,Adipose tissue,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
229 | C | 0925-2312 |
Citations | PageRank | References |
4 | 0.46 | 15 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Jiang | 1 | 37 | 17.89 |
| Huating Li | 2 | 26 | 2.01 |
| Xuhong Hou | 3 | 47 | 4.03 |
| Bin Sheng | 4 | 368 | 61.19 |
| Rui-min Shen | 5 | 941 | 85.45 |
| Xiaoyang Liu | 6 | 270 | 34.49 |
| Weiping Jia | 7 | 29 | 3.74 |
| Ping Li | 8 | 202 | 40.76 |
| Ruogu Fang | 9 | 287 | 21.78 |