A bi-directional segmentation method for prostate ultrasound images under semantic constraints
Abstract Due to the lack of sufficient labeled data for the prostate and the extensive and complex semantic information in ultrasound images, accurately and quickly segmenting the prostate in transrectal ultrasound (TRUS) images remains a challenging task. In this context, this paper proposes a solu...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-61238-5 |
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| _version_ | 1846121792960200704 |
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| author | Zexiang Li Wei Du Yongtao Shi Wei Li Chao Gao |
| author_facet | Zexiang Li Wei Du Yongtao Shi Wei Li Chao Gao |
| author_sort | Zexiang Li |
| collection | DOAJ |
| description | Abstract Due to the lack of sufficient labeled data for the prostate and the extensive and complex semantic information in ultrasound images, accurately and quickly segmenting the prostate in transrectal ultrasound (TRUS) images remains a challenging task. In this context, this paper proposes a solution for TRUS image segmentation using an end-to-end bidirectional semantic constraint method, namely the BiSeC model. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the Dice Similarity Coefficient (DSC) of 96.74% and the Intersection over Union (IoU) of 93.71%. Our model achieves a good balance between actual boundaries and noise areas, reducing costs while ensuring the accuracy and speed of segmentation. |
| format | Article |
| id | doaj-art-671e6e16eff941f3abb28733faeea5b3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-671e6e16eff941f3abb28733faeea5b32024-12-15T12:08:50ZengNature PortfolioScientific Reports2045-23222024-05-0114111810.1038/s41598-024-61238-5A bi-directional segmentation method for prostate ultrasound images under semantic constraintsZexiang Li0Wei Du1Yongtao Shi2Wei Li3Chao Gao4College of Electrical Engineering and New Energy, China Three Gorges UniversityCollege of Computer and Information Technology, China Three Gorges UniversityCollege of Computer and Information Technology, China Three Gorges UniversityCollege of Computer and Information Technology, China Three Gorges UniversityCollege of Computer and Information Technology, China Three Gorges UniversityAbstract Due to the lack of sufficient labeled data for the prostate and the extensive and complex semantic information in ultrasound images, accurately and quickly segmenting the prostate in transrectal ultrasound (TRUS) images remains a challenging task. In this context, this paper proposes a solution for TRUS image segmentation using an end-to-end bidirectional semantic constraint method, namely the BiSeC model. The experimental results show that compared with classic or popular deep learning methods, this method has better segmentation performance, with the Dice Similarity Coefficient (DSC) of 96.74% and the Intersection over Union (IoU) of 93.71%. Our model achieves a good balance between actual boundaries and noise areas, reducing costs while ensuring the accuracy and speed of segmentation.https://doi.org/10.1038/s41598-024-61238-5 |
| spellingShingle | Zexiang Li Wei Du Yongtao Shi Wei Li Chao Gao A bi-directional segmentation method for prostate ultrasound images under semantic constraints Scientific Reports |
| title | A bi-directional segmentation method for prostate ultrasound images under semantic constraints |
| title_full | A bi-directional segmentation method for prostate ultrasound images under semantic constraints |
| title_fullStr | A bi-directional segmentation method for prostate ultrasound images under semantic constraints |
| title_full_unstemmed | A bi-directional segmentation method for prostate ultrasound images under semantic constraints |
| title_short | A bi-directional segmentation method for prostate ultrasound images under semantic constraints |
| title_sort | bi directional segmentation method for prostate ultrasound images under semantic constraints |
| url | https://doi.org/10.1038/s41598-024-61238-5 |
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