CDA-Net for Automatic Prostate Segmentation in MR Images
Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6678 |
| _version_ | 1797552738154840064 |
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| author | Zhiying Lu Mingyue Zhao Yong Pang |
| author_facet | Zhiying Lu Mingyue Zhao Yong Pang |
| author_sort | Zhiying Lu |
| collection | DOAJ |
| description | Automatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance. |
| first_indexed | 2024-03-10T16:05:22Z |
| format | Article |
| id | doaj.art-2708a5b8b7f54b1a83f135489837cddb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:05:22Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2708a5b8b7f54b1a83f135489837cddb2023-11-20T14:55:51ZengMDPI AGApplied Sciences2076-34172020-09-011019667810.3390/app10196678CDA-Net for Automatic Prostate Segmentation in MR ImagesZhiying Lu0Mingyue Zhao1Yong Pang2School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaInstitute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, ChinaAutomatic and accurate prostate segmentation is an essential prerequisite for assisting diagnosis and treatment, such as guiding biopsy procedures and radiation therapy. Therefore, this paper proposes a cascaded dual attention network (CDA-Net) for automatic prostate segmentation in MRI scans. The network includes two stages of RAS-FasterRCNN and RAU-Net. Firstly, RAS-FasterRCNN uses improved FasterRCNN and sequence correlation processing to extract regions of interest (ROI) of organs. This ROI extraction serves as a hard attention mechanism to focus the segmentation of the subsequent network on a certain area. Secondly, the addition of residual convolution block and self-attention mechanism in RAU-Net enables the network to gradually focus on the area where the organ exists while making full use of multiscale features. The algorithm was evaluated on the PROMISE12 and ASPS13 datasets and presents the dice similarity coefficient of 92.88% and 92.65%, respectively, surpassing the state-of-the-art algorithms. In a variety of complex slice images, especially for the base and apex of slice sequences, the algorithm also achieved credible segmentation performance.https://www.mdpi.com/2076-3417/10/19/6678image segmentationprostate MR imageCDA-NetROI extractionsequence correlation processingself-attention mechanism |
| spellingShingle | Zhiying Lu Mingyue Zhao Yong Pang CDA-Net for Automatic Prostate Segmentation in MR Images Applied Sciences image segmentation prostate MR image CDA-Net ROI extraction sequence correlation processing self-attention mechanism |
| title | CDA-Net for Automatic Prostate Segmentation in MR Images |
| title_full | CDA-Net for Automatic Prostate Segmentation in MR Images |
| title_fullStr | CDA-Net for Automatic Prostate Segmentation in MR Images |
| title_full_unstemmed | CDA-Net for Automatic Prostate Segmentation in MR Images |
| title_short | CDA-Net for Automatic Prostate Segmentation in MR Images |
| title_sort | cda net for automatic prostate segmentation in mr images |
| topic | image segmentation prostate MR image CDA-Net ROI extraction sequence correlation processing self-attention mechanism |
| url | https://www.mdpi.com/2076-3417/10/19/6678 |
| work_keys_str_mv | AT zhiyinglu cdanetforautomaticprostatesegmentationinmrimages AT mingyuezhao cdanetforautomaticprostatesegmentationinmrimages AT yongpang cdanetforautomaticprostatesegmentationinmrimages |