Article ; Online: Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
2021 Volume 2021, Page(s) 3582–3585
Abstract: Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D ... ...
| Abstract | Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring. Recently, deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. However, 2D DCNNs cannot fully leverage the inter-slice information, while 3D DCNNs are computationally expensive and memory intensive. To address these issues, we first propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs, thereby improving the model performance. Moreover, we design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency. Extensive experiments on the LiTS dataset have demonstrated the superiority of the proposed method.Clinical relevance- The proposed method can effectively segment livers and tumors from CT scans with low complexity, which can be easily implemented into clinical practice. |
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| MeSH term(s) | Abdomen ; Humans ; Image Processing, Computer-Assisted ; Liver/diagnostic imaging ; Neoplasms ; Neural Networks, Computer |
| Language | English |
| Publishing date | 2021-12-07 |
| Publishing country | United States |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ISSN | 2694-0604 |
| ISSN (online) | 2694-0604 |
| DOI | 10.1109/EMBC46164.2021.9629698 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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