S1550
Physics - Autosegmentation
ESTRO 2026
Conclusion: AI-based auto-segmentation with MVision® demonstrated excellent agreement with manual contours for the four cardiac chambers, achieving DSC values >0.78 in all cases and >0.85 for the left ventricle. The algorithm’s performance was comparable to that of experienced physicians, with no significant differences across expertise levels. The coronary arteries showed the lowest DSC values (<0.4), consistent with their small volume and poor visibility on standard planning CT. Both human and AI contours exhibited similar limitations, highlighting the need for multimodality imaging or model retraining for these structures.Overall, these findings support the safe integration of AI-assisted cardiac contouring into clinical workflows. AI provides reliable delineations for major cardiac chambers, reduces contouring time, and offers a consistent starting point for expert review,
Material/Methods We assembled 322 pelvic CT volumes; 215 local datasets and 107 from The Cancer Imaging Archive (TCIA) and split them into two groups (258 train, 64 validation). A lightweight 2D U-Net encoder was first pretrained without labels to predict the subsequent CT slice from tri-planar inputs (axial, sagittal, coronal), then was fine-tuned (Figure 1) for multi-organ segmentation of bladder, femoral heads, penile bulb (PB), rectum, prostate and seminal vesicles (SV). Baselines were identical U-Nets trained from scratch with 1-channel and 3-channel inputs to isolate representation-learning effects from input formatting. Two regimens were studied: a full-label dataset (n=258) and a reduced-label dataset (n=60) to emulate limited-annotation settings. Evaluation on held-out patients used Dice Similarity Coefficient (DSC) and Mean Distance Agreement (MDA, mm). Paired t-tests with Bonferroni correction (significance p<0.0083) were used to assess differences. Implementation remained lightweight, with no teacher–student distillation or pseudo-labelling and training and evaluation were identical across each model.
thereby contributing to improved efficiency, standardization, and patient safety in breast radiotherapy planning. References:
Tsang Y, Hoskin P, Spezi E, Landau D, Lester J, Miles E, Conibear J. Assessment of contour variability in target volumes and organs at risk in lung cancer radiotherapy. Technical Innovations & Patient Support in Radiation Oncology. 2019;10:8–12González-del Portillo E, Hernández-Rodríguez J, Tenllado-Baena E, Fernández-Lara Á, Alonso-Rodríguez O, Matías-Pérez Á, Cigarral-García C, García-Álvarez G, Pérez- Romasanta LA.Cardiac segments dosimetric benefit from deep inspiration breath hold technique for left- sided breast cancer radiotherapy.Rep Pract Oncol Radiother. 2024;29(2): Keywords: Cardiac structures, Contouring variability, IA Learning Anatomy from Unlabelled CT Volumes: An Unsupervised Framework for Improving Prostate Radiotherapy Segmentation Diyana Afrina Hizam, Ngie Min Ung, Marniza Saad, Firdaus Mohd Salleh, Asyraf Muaadz, Li Kuo Tan Department of Clinical Oncology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia Purpose/Objective To test whether unsupervised next-slice prediction pretraining can improve pelvic CT organ segmentation for prostate radiotherapy when labels are limited. To our knowledge, this is the first report of using a novel lightweight 2D U-Net with next-slice prediction as a general pretraining strategy for CT segmentation. Digital Poster 765
Results The novel lightweight 2D U-Net next-slice pretraining improved boundary accuracy across structures. In the full-label dataset settings, MDA decreased for bladder (0.779 → 0.547 mm), femoral heads (0.900 → 0.669 mm), PB (1.688 → 1.283 mm), rectum (1.443 → 0.994 mm), prostate (1.474 → 1.183 mm), and SV (1.201 → 0.893 mm). In a direct data efficiency comparison, the unsupervised model in the reduced-label dataset settings (n=60) achieved MDA comparable to or better than a fully supervised scratch model trained on the full-label dataset (n=258), with the largest gains for small/low-contrast organs (PB, SV, rectum). Organ-wise MDA (lower is better) for pretrained-reduced versus scratch-full is shown in Figure 2. Using three identical input channels did not improve over a single channel, indicating that the benefit arises from representation learning rather than input format. Several organ-wise MDA reductions remained significant after Bonferroni correction (p<0.0083).
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