Enhancing Precision in Rectal Cancer Radiotherapy: Localized Fine-Tuning of Deep-learning based Auto-segmentation (DLAS) Model for Clinical Target Volume and Organs-at-risk
Jianhao Geng,
Xin Sui,
Rongxu Du
et al.
Abstract:Background and Purpose
Various deep learning auto-segmentation (DLAS) models have been proposed, some of which commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This study aims to enhance precision of a popular commercial DLAS product in rectal cancer radiotherapy by localized fine-tuning, addressing challenges in practicality and generalizability in real-world clinical settings.
Materials and Methods
A total of 120 Stage II/III mid-lo… Show more
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