Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2019
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| _version_ | 1866911787313528832 |
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| author | Wang, Yi Dou, Haoran Hu, Xiaowei Zhu, Lei Yang, Xin Xu, Ming Qin, Jing Heng, Pheng-Ann Wang, Tianfu Ni, Dong |
| author_facet | Wang, Yi Dou, Haoran Hu, Xiaowei Zhu, Lei Yang, Xin Xu, Ming Qin, Jing Heng, Pheng-Ann Wang, Tianfu Ni, Dong |
| contents | Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1907_01743 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound Wang, Yi Dou, Haoran Hu, Xiaowei Zhu, Lei Yang, Xin Xu, Ming Qin, Jing Heng, Pheng-Ann Wang, Tianfu Ni, Dong Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D. |
| title | Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/1907.01743 |