S1551
Physics - Autosegmentation
ESTRO 2026
1 Medical Physics & Clinical engineering, Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom. 2 Urology, Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom. 3 Clinical Oncology, Guy’s & St. Thomas’ NHS Foundation Trust, London, United Kingdom. 4 School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom. 5 AI & Data Analytics, FH Technikum Wien, Vienna, Austria Purpose/Objective: In radiotherapy, accurate segmentation of organs at risk on planning CT scans is critical for effective treatment. A deep-learning (DL) pelvic auto- segmentation model developed at Guy’s and St. Thomas’ NHS Foundation Trust [1] has reduced manual segmentation workload [2], whilst aiming to improve consistency. This study evaluates the model’s performance using quantitative metrics over three distinct stages of clinical implementation – testing, prospective evaluation, and embedded clinical service – to assess its continued utility, accuracy, stability and potential user automation bias over time. Material/Methods: The segmentation model was evaluated at three distinct stages: at model testing (stage 1, datasets n = 10), prospective clinical evaluation (stage 2, n = 20), and embedded clinical service (stage 3, n = 20). Datasets in stage 3 were auto-segmented a median of 88 days after day 1 of clinical implementation (range 63 days to 110 days). Accuracy was assessed using volumetric Dice Similarity Coefficient (vDSC) and surface DSC (sDSC) against clinician-approved structures. Statistical significance between time-points was evaluated using a Mann-Whitney U test (p,U). Results:
Conclusion Unsupervised next-slice prediction pretraining with a novel lightweight 2D U-Net improves pelvic CT segmentation and reduces annotation needs. It achieves clinically relevant performance with fewer labels, with clear benefit for small, low-contrast organs. Because it avoids pseudo-labels and heavy teacher–student frameworks, the method is simple and computationally light, supporting adoption in resource-constrained radiotherapy workflows. References 1. Wolf, D., et al., Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging. Scientific Reports, 2023. 13(1): p. 20260. 2. Isensee, F., et al., nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 2021. 18(2): p. 203- 211. 3. Peng, J., et al., Boundary-aware information maximization for self-supervised medical image segmentation. Medical Image Analysis, 2024. 94: p. 103150. 4. Dominic, J., et al., Improving data-efficiency and robustness of medical imaging segmentation using inpainting-based self-supervised learning. Bioengineering, 2023. 10(2): p. 207. Keywords Next-slice prediction, Unsupervised, Segmentation
Digital Poster 959
Evaluating the Performance and Consistency of a Deep-Learning Pelvic Auto-Segmentation Model Across Clinical Stages in Radiotherapy Evi Markou 1 , Victoria Butterworth 1 , Luis Ribeiro 2 , Priyankah Patel 3 , Benjamin Taylor 3 , Ingrid White 3 , Anna Winship 3 , Andrew King 4 , Isabel Dregely 5 , Sally Barrington 4 , Teresa Guerrero Urbano 3 , Christopher Thomas 1
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