Automatic prompt-guided incremental fine-tuning for offset detection in radiotherapy patient positioning

Objective.Radiotherapy (RT) requires accurate and consistent patient positioning to ensure precise radiation delivery and minimize unnecessary exposure to healthy tissues. Conventional workflows rely heavily on clinicians' experience and repeated CT-based registration, leading to inefficiency, patient discomfort, and potential alignment inconsistencies. This work aims to develop an automatic, robust, and low-cost posture offset detection method that overcomes these limitations.Approach.We propose a prompt-guided incremental fine-tuning model built upon a large-scale image segmentation backbone. The system captures real-time two-dimensional images from a single RGB camera and automatically generates adaptive prompt points based on individual body shapes and postures, improving segmentation robustness and reducing environmental interference. An incremental fine-tuning strategy enables continuous adaptation to newly collected patient images throughout the treatment cycle. Furthermore, a multi-level offset analysis framework is introduced, integrating contour-level, keypoint-level, and pixel-level estimations to identify, localize, and quantify posture deviations across multiple granularities. The system is deployed clinically to collect real RT data and construct a dedicated validation dataset.Main Results.Extensive experiments on real clinical data show that the proposed method achieves accurate, fast, and stable posture offset detection. It substantially improves positioning consistency and efficiency compared with conventional workflows. Ablation studies further demonstrate the effectiveness and necessity of each module within the framework.Significance.This study provides a practical and low-cost solution for RT positioning, reducing clinician workload and patient burden while improving treatment accuracy. It demonstrates the potential of prompt-guided incremental adaptation and multi-level offset analysis in real RT environments, offering a promising direction for intelligent, automated RT positioning systems.