Deep learning-based dynamic generation of uterine geometry for cervical cancer radiotherapy
10.3760/cma.j.cn113030-20241031-00420
- VernacularTitle:基于深度学习的宫颈癌放疗子宫几何轮廓动态生成
- Author:
Batuer MAIDINA
1
;
Jinghan HUANG
;
Chuanghui ZHOU
;
Junyuan ZHONG
;
Lei YANG
;
Linghong ZHOU
;
Xia LI
;
Genggeng QIN
Author Information
1. 南方医科大学生物医学工程学院，广州 510515
- Publication Type:Journal Article
- Keywords:
Uterine cervical neoplasms;
Radiotherapy;
Conditional generative adversarial network;
Geometric boundary;
Organ motion
- From:
Chinese Journal of Radiation Oncology
2025;34(6):585-593
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To propose a semi-supervised learning method for dynamic generation of organ geometric contours, leveraging bladder volume variations and its relative position to the uterus to accurately generate uterine contours in cervical cancer radiotherapy.Methods:A total of 120 sets of pelvic planning CT images (including both full and empty bladder scans) from 60 patients with cervical cancer treated at the Department of Radiation Oncology, Nanfang Hospital of Southern Medical University between January and December 2023 were retrospectively collected. A conditional generative adversarial network (CGAN) based on a squeeze-and-excitation channel attention mechanism was proposed to accurately generate uterine geometric contours under varying bladder filling states. By emphasizing the critical spatial relationships between the bladder and uterus, the model learned the relative anatomical positions of pelvic organs and their motion correlations. The generative performance was quantitatively evaluated using the average Dice similarity coefficient (DSC), intersection over union (IoU), and the 95 th percentile Hausdorff distance (HD95), and was compared with GAN model, CGAN model, and Pix2Pix model. Pairwise comparisons were perfomed by paired-sample t-test. Results:The proposed SE-CGAN model achieved the best performance on the test set, with DSC of 0.83±0.09, IoU of 0.71±0.05, HD95 of (6.74±1.23) mm, improving DSC by 7.5%, 4.9%, and 3.6% compared to the GAN, CGAN, and Pix2Pix models, respectively (all P<0.001), and reducing the mean HD95 by 32.9%-45.3%. Statistical analysis revealed significant differences between SE-CGAN model and the other 3 baseline models, whereas no significant difference was observed between CGAN model and Pix2Pix model. The visualization results further demonstrated that the GAN model produced uterine contours deviated greatly from the real shape, and the edge was fuzzy; CGAN and Pix2Pix model achieved better overlap but lacked of precision in boundary reconstruction. In contrast, the contours generated by SE-CGAN model closely matched the ground truth with clearly defined edges, indicating superior reconstruction accuracy. Conclusions:In this study, we propose a generative adversarial network method that establishes a dynamic modulation mechanism by which the bladder state influences the uterine geometric contour, enabling accurate generation of the uterine contours from the bladder contours of any given localization CT scan. This approach effectively addresses the uncertainty in radiotherapy target delineation caused by pelvic organ motion.
