Enhancing Adaptive Radiotherapy Segmentation with a 3D Unet Framework and Prior Fraction Information
Recommended Citation
Li C, Rusu DN, Dolan JL, Parikh PJ, Thind KS. Enhancing Adaptive Radiotherapy Segmentation with a 3D Unet Framework and Prior Fraction Information. Med Phys 2025; 52(10):303-304.
Document Type
Conference Proceeding
Publication Date
9-30-2025
Publication Title
Med Phys
Keywords
abdominal viscera, adaptive radiation, adult, benchmarking, cancer patient, conference abstract, cross validation, differential scanning calorimetry, duodenum, human, pancreas cancer, radiotherapy, retrospective study, sequential analysis, small intestine, stomach
Abstract
Purpose: The time and resource demands of online Adaptive Radiation Therapy (ART) can limit its widespread clinical adoption and potentially impact patient throughput. To address this, we developed a 3D UNet-based sequential segmentation framework that leverages information from prior fraction images and segmentation to enhance accuracy and efficiency of the segmentation process. Methods: This retrospective study analyzed a dataset of 23 pancreatic cancer patients undergoing 5-fraction online adaptive MR-guided radiotherapy (MRgRT), focusing on the segmentation of four abdominal organs: colon, duodenum, small bowel, and stomach. Preprocessing included resampling to voxel spacing of 1.5 × 1.5 × 3.0 mm and cropping input volumes to 128 × 128 × 64. The proposed sequential segmentation framework integrates current and prior fraction information using a dual-path architecture with feature fusion blocks. We conducted three-fold cross-validation for model evaluation, ensuring patient-wise separation to prevent data leakage. Model performance was compared to a baseline 3D UNet without sequential support using three metrics: Dice Similarity Coefficient (DSC) for volumetric overlap, 95th percentile Hausdorff Distance (HD95) for surface distance, and Average Symmetric Surface Distance (ASSD) for boundary differences. Results: The sequential method consistently outperformed the baseline across all organs. For the colon, DSC improved from 0.834 to 0.858 (p=0.049), HD95 decreased from 10.82mm to 8.45mm (p=0.048), and ASSD reduced from 1.73mm to 1.39mm (p=0.036). Improvements were also observed in the duodenum (DSC: 0.713 to 0.719), small bowel (DSC: 0.766 to 0.780), and stomach (DSC: 0.876 to 0.885), although not statistically significant. The most substantial improvements were consistently found in colon segmentation. Conclusion: The proposed 3D UNet-based sequential segmentation framework effectively leverages prior fraction information to enhance segmentation accuracy and precision in adaptive radiotherapy. This approach, adaptable to other UNet-like architectures, holds significant promise for improving the efficiency and effectiveness of clinical segmentation tasks, ultimately benefiting patient care.
Volume
52
Issue
10
First Page
303
Last Page
304
