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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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-94431-1 |
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| Summary: | 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. |
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| ISSN: | 2045-2322 |