An experimental study of U-net variants on liver segmentation from CT scans
The liver, a complex and important organ in the human body, is crucial to many physiological processes. For the diagnosis and ongoing monitoring of a wide spectrum of liver diseases, an accurate segmentation of the liver from medical imaging is essential. The importance of liver segmentation in clin...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-03-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys-2024-0185 |
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| author | Halder Akash Sau Arup Majumder Surya Kaplun Dmitrii Sarkar Ram |
| author_facet | Halder Akash Sau Arup Majumder Surya Kaplun Dmitrii Sarkar Ram |
| author_sort | Halder Akash |
| collection | DOAJ |
| description | The liver, a complex and important organ in the human body, is crucial to many physiological processes. For the diagnosis and ongoing monitoring of a wide spectrum of liver diseases, an accurate segmentation of the liver from medical imaging is essential. The importance of liver segmentation in clinical practice is examined in this research, along with the difficulties in attaining accurate segmentation masks, particularly when working with small structures and precise details. This study investigates the performance of ten well-known U-Net models, including Vanilla U-Net, Attention U-Net, V-Net, U-Net 3+, R2U-Net, U2{{\rm{U}}}^{2}-Net, U-Net++, Res U-Net, Swin-U-Net, and Trans-U-Net. These variations have become optimal approaches to liver segmentation, each providing certain benefits and addressing particular difficulties. We have conducted this research on computed tomography scan images from three standard datasets, namely, 3DIRCADb, CHAOS, and LiTS datasets. The U-Net architecture has become a mainstay in contemporary research on medical picture segmentation due to its success in preserving contextual information and capturing fine features. The structural and functional characteristics that help it perform well on liver segmentation tasks even with scant annotated data are well highlighted in this study. The code and additional results can be found in the Github https://github.com/akalder/ComparativeStudyLiverSegmentation. |
| format | Article |
| id | doaj-art-eaa9d71db2144c0a9721bdd99eb952f9 |
| institution | OA Journals |
| issn | 2191-026X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj-art-eaa9d71db2144c0a9721bdd99eb952f92025-08-20T01:58:18ZengDe GruyterJournal of Intelligent Systems2191-026X2025-03-013411040357110.1515/jisys-2024-0185An experimental study of U-net variants on liver segmentation from CT scansHalder Akash0Sau Arup1Majumder Surya2Kaplun Dmitrii3Sarkar Ram4Department of Computer Science and Engineering, Jadavpur University, Jadavpur, Kolkata, West Bengal 700032, IndiaDepartment of Computer Science and Engineering, Institute of Engineering and Management, Kolkata 700032, IndiaDepartment of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, West Bengal 700107, IndiaDepartment of Automation and Control Processes, Saint Petersburg Electrotechnical University “LETI”, St Petersburg 197022, RussiaDepartment of Computer Science and Engineering, Jadavpur University, Jadavpur, Kolkata, West Bengal 700032, IndiaThe liver, a complex and important organ in the human body, is crucial to many physiological processes. For the diagnosis and ongoing monitoring of a wide spectrum of liver diseases, an accurate segmentation of the liver from medical imaging is essential. The importance of liver segmentation in clinical practice is examined in this research, along with the difficulties in attaining accurate segmentation masks, particularly when working with small structures and precise details. This study investigates the performance of ten well-known U-Net models, including Vanilla U-Net, Attention U-Net, V-Net, U-Net 3+, R2U-Net, U2{{\rm{U}}}^{2}-Net, U-Net++, Res U-Net, Swin-U-Net, and Trans-U-Net. These variations have become optimal approaches to liver segmentation, each providing certain benefits and addressing particular difficulties. We have conducted this research on computed tomography scan images from three standard datasets, namely, 3DIRCADb, CHAOS, and LiTS datasets. The U-Net architecture has become a mainstay in contemporary research on medical picture segmentation due to its success in preserving contextual information and capturing fine features. The structural and functional characteristics that help it perform well on liver segmentation tasks even with scant annotated data are well highlighted in this study. The code and additional results can be found in the Github https://github.com/akalder/ComparativeStudyLiverSegmentation.https://doi.org/10.1515/jisys-2024-0185liver cancerliver segmentationmedical imagingct scanu-netdeep learning |
| spellingShingle | Halder Akash Sau Arup Majumder Surya Kaplun Dmitrii Sarkar Ram An experimental study of U-net variants on liver segmentation from CT scans Journal of Intelligent Systems liver cancer liver segmentation medical imaging ct scan u-net deep learning |
| title | An experimental study of U-net variants on liver segmentation from CT scans |
| title_full | An experimental study of U-net variants on liver segmentation from CT scans |
| title_fullStr | An experimental study of U-net variants on liver segmentation from CT scans |
| title_full_unstemmed | An experimental study of U-net variants on liver segmentation from CT scans |
| title_short | An experimental study of U-net variants on liver segmentation from CT scans |
| title_sort | experimental study of u net variants on liver segmentation from ct scans |
| topic | liver cancer liver segmentation medical imaging ct scan u-net deep learning |
| url | https://doi.org/10.1515/jisys-2024-0185 |
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