Name
Abstract | ||
|---|---|---|
Liver cancer is one of the cancers with the highest mortality. In order to help doctors diagnose and treat liver lesion, an automatic liver segmentation model is urgently needed due to manually segmentation is time-consuming and error-prone. In this paper, we propose a nested attention-aware segmentation network, named Attention UNet++. Our proposed method has a deep supervised encoder-decoder architecture and a redesigned dense skip connection. Attention UNet++ introduces attention mechanism between nested convolutional blocks so that the features extracted at different levels can be merged with a task-related selection. Besides, due to the introduction of deep supervision, the prediction speed of the pruned network is accelerated at the cost of modest performance degradation. We evaluated proposed model on MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset. Attention UNet++ achieved very competitive performance for liver segmentation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190761 | 2020 IEEE International Conference on Image Processing (ICIP) |
Keywords | DocType | ISSN |
Image segmentation,Liver,Logic gates,Feature extraction,Computed tomography,Task analysis,Cancer | Conference | 1522-4880 |
ISBN | Citations | PageRank |
978-1-7281-6395-6 | 1 | 0.36 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Li | 1 | 1 | 1.71 |
| Yusong Tan | 2 | 1 | 2.72 |
| Wei Chen | 3 | 1 | 0.69 |
| Xin Luo | 4 | 44 | 5.16 |
| Yuanming Gao | 5 | 1 | 0.36 |
| Xiaogang Jia | 6 | 1 | 0.69 |
| Zhiying Wang | 7 | 1 | 0.36 |