S1545
Physics - Autosegmentation
ESTRO 2026
sparse geometric prompt types to produce GTV segmentation masks (Figure 1): a single central point alone (Point) and with six external points (Points7), 2D bounding boxes in the axial plane (Box1) and in 3D (Box3), and 2D masks on one axial slice (Mask1) or three orthogonal slices (Mask3).Three promptable foundation models were evaluated: Segment Anything 2 (SAM2), SAM2 fine-tuned for medical imaging (MedSAM2), and nnInteractive (nnI), an nnUNet-based model trained for interactive medical segmentation.A multi-institutional (N=5) dataset of 580 clinical GTV delineations in volumetric MRI from 0.35T MRI-linacs was compiled with GTVs from abdomen (56 cases), lung (215), liver (110), pancreas (53), and pelvis (146) sites. Treatment planning MRI images were acquired with various imaging parameters and acquisition settings, reflecting differences in equipment and protocols across involved institutions.The output of a given model-prompt combination was compared to the ground truth using various metrics, including the Dice similarity coefficient (DSC).
produced overall better results (median DSC over all prompt types 0.75 for nnInteractive, 0.70 for MedSAM2, 0.5 for SAM2).Performance showed modest variability across anatomical sites, reflecting differences in MRI contrast and image quality. Performance was highest for liver and lung sites, followed by abdominal and pancreatic sites, and lowest for pelvic cases.
Conclusion: Promptable foundation models can effectively produce GTV delineations on 0.35T MR images from MR-linacs across multiple anatomical sites when provided with high-information prompts. Performance approaches that of specialised, task-specific models, suggesting strong potential for integration into the general MRIgRT segmentation workflows. References: 1 Betancourt Tarifa et al. "Pancreatic Tumor Segmentation in Therapeutic and Diagnostic MRI." (2025) DOI: 10.5281/zenodo.15081832 Leaderboard Task 2. URL: https://panther.grand- challenge.org/evaluation/closed-testing-phase-task- 2/leaderboard/ Keywords: MRI-linac,GTV segmentation Digital Poster 335 Evaluation of SAM2 model performance and its clinical aspects for ultrasound image segmentation Fruzsina Dvorzsak 1 , Maria Prosszer 1 , Alinka Olajos- Horvath 1 , Anna Fronto 1 , Greta Czibere 1 , Tamas Nadasi 1 , Veronika Donka 1 , Dora Bianka Dr Koczka- Balogh 2 , Krisztian Koos 1 1 HC STO-Artificial Intelligence & Machine Learning, GE Healthcare Magyarország Kft., Budapest, Hungary.
Results: Promptable foundation models achieved
segmentation performance with median DSCs of up to 0.85 (nnInteractive-Mask3) with an interquartile range of 0.80-0.89. AI-assistance thus surpassed automated domain-specific approaches, such as those of the PANTHER challenge with mean DSCs up to 0.53 for pancreas GTVs on 1.5T MRI images. (1)Prompts with more spatial information (especially three masks in orthogonal planes) yielded better results with reduced variability. This effect was less prominent for nnInteractive and MedSAM2, which were trained/fine- tuned for medical images. These specialised models
2 Department of Obstetrics and Gynecology, Semmelweis University, Budapest, Hungary
Made with FlippingBook - Share PDF online