A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy
Abstract This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using 694 CT scans from 179 cervical cancer pat...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94431-1 |
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| author | Lele Zang Jing Liu Huiqi Zhang Shitao Zhu Mingxuan Zhu Yuqin Wang Yaxin Kang Jihong Chen Qin Xu |
| author_facet | Lele Zang Jing Liu Huiqi Zhang Shitao Zhu Mingxuan Zhu Yuqin Wang Yaxin Kang Jihong Chen Qin Xu |
| author_sort | Lele Zang |
| collection | DOAJ |
| description | Abstract This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using 694 CT scans from 179 cervical cancer patients, the performance of five models (AM-UNet, UNet, DeepLab V3, UNETR and nnU-Net) was compared. The models were assessed using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and dose-volume index (DVI). AM-UNet achieved mean DSCs of 0.862, 0.937, 0.823, and 0.725 for HRCTV, bladder, rectum, and sigmoid, respectively. Subjective evaluations showed 93.07% of AM-UNet predicted HRCTV were rated as clinically acceptable or needing minor adjustments, with no unacceptable cases. Dosimetric differences between AM-UNet-generated and manually delineated contours were within 1%, highlighting its potential for improving clinical workflows in brachytherapy. |
| format | Article |
| id | doaj-art-7a966a734e9940d68ef4f0a6dbaa84c0 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7a966a734e9940d68ef4f0a6dbaa84c02025-08-20T02:49:26ZengNature PortfolioScientific Reports2045-23222025-03-0115111010.1038/s41598-025-94431-1A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapyLele Zang0Jing Liu1Huiqi Zhang2Shitao Zhu3Mingxuan Zhu4Yuqin Wang5Yaxin Kang6Jihong Chen7Qin Xu8Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Radiation Oncology, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical UniversityCollege of Computer and Data Science, Fuzhou UniversityDepartment of Radiation Oncology, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical UniversityDepartment of Radiation Oncology, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical UniversityDepartment of Radiation Oncology, Fujian Cancer Hospital, Clinical Oncology School of Fujian Medical UniversityDepartment of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalDepartment of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer HospitalAbstract This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs) in cervical cancer brachytherapy. Using 694 CT scans from 179 cervical cancer patients, the performance of five models (AM-UNet, UNet, DeepLab V3, UNETR and nnU-Net) was compared. The models were assessed using the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and dose-volume index (DVI). AM-UNet achieved mean DSCs of 0.862, 0.937, 0.823, and 0.725 for HRCTV, bladder, rectum, and sigmoid, respectively. Subjective evaluations showed 93.07% of AM-UNet predicted HRCTV were rated as clinically acceptable or needing minor adjustments, with no unacceptable cases. Dosimetric differences between AM-UNet-generated and manually delineated contours were within 1%, highlighting its potential for improving clinical workflows in brachytherapy.https://doi.org/10.1038/s41598-025-94431-1Cervical cancerBrachytherapyAuto-segmentationDeep learningComputed |
| spellingShingle | Lele Zang Jing Liu Huiqi Zhang Shitao Zhu Mingxuan Zhu Yuqin Wang Yaxin Kang Jihong Chen Qin Xu A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy Scientific Reports Cervical cancer Brachytherapy Auto-segmentation Deep learning Computed |
| title | A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy |
| title_full | A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy |
| title_fullStr | A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy |
| title_full_unstemmed | A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy |
| title_short | A deep learning model based on Mamba for automatic segmentation in cervical cancer brachytherapy |
| title_sort | deep learning model based on mamba for automatic segmentation in cervical cancer brachytherapy |
| topic | Cervical cancer Brachytherapy Auto-segmentation Deep learning Computed |
| url | https://doi.org/10.1038/s41598-025-94431-1 |
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