S1546
Physics - Autosegmentation
ESTRO 2026
Purpose/Objective: Segment Anything Model (SAM) is a powerful framework designed for interactive segmentation across various image types, including medical images such as MRI, CT and ultrasound. In this study, the performance of the model was evaluated on ultrasound images from various gynecological examinations. We used the adapted model – SAM2 – for this evaluation. Trained on videos, SAM2 handles dynamic ultrasound data better than SAM. Delineation of selected organs was performed in 3D Slicer software using a custom plugin. Material/Methods: 3D Slicer was extended with a plugin to integrate SAM2 via an interactive segmentation module. The model operates by standard prompts (inside/outside points) and can be limited along third axis with a bounding box. The workflow starts with generating an initial 2D contour using SAM2, which is refined manually before propagating through slices within the bounding box.20 gynecological scans were contoured for three organs – ovary, uterus, endometrium – using two methods: manual contouring tools and SAM2 (with and without manual refinement). The dataset included 2D images, 3D volumes and cineloops. Contours were generated by five observers (except one 3D endometrium case, completed by four observers) and compared against ground truth contours provided by clinical experts (Figure 1). Dice Similarity Coefficient (DSC) was calculated for each organ. Annotation time for each case was recorded.
minutes). For uterus scans, this reduction is statistically significant (p<0.05) across all data types. On cineloops, SAM2 consistently helps to achieve more accurate contours in less time. In terms of delineation, the endometrium seems to be the most challenging organ (both manually and with SAM2), due to its variable appearance and indistinct boundaries.
Table 1. Average annotation time and quality of manual, SAM2 and refined SAM2 contours. Conclusion: This study highlights the potential of SAM2 for medical image segmentation. For selected organs, manual correction of SAM2-generated pre-contours can yield higher-quality results than fully manual contouring while reducing annotation time. SAM2 can serve as a baseline for future models, though finetuned approaches may offer greater precision. Our findings also emphasize the critical importance of expert knowledge in achieving high-quality contours across imaging applications. References: Deák-Karancsi, B., Karancsi, Z., Kékesi, Á., Kiss, E. K., Hakim, L. M., Koós, K., & Ruskó, L. (2025). 2511 Evaluation of SAM and SAM2 from clinical perspective for organ delineation. Radiotherapy and Oncology, 206, S4360-S4363. Dong, H., Gu, H., Chen, Y., Yang, J., Chen, Y., & Mazurowski, M. A. (2024). Segment anything model 2: an application to 2d and 3d medical images. arXiv preprint arXiv:2408.00756. Ning, G., Liang, H., Jiang, Z., Zhang, H., & Liao, H. (2023). The potential of 'Segment Anything'(SAM) for universal intelligent ultrasound image guidance. Bioscience trends, 17(3), 230-233. Keywords: Segment Anything, organ delineation, ultrasound
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Impact of uncertainty prediction on manual editing of rectal cancer CTV auto-segmentations Federica Carmen Maruccio 1 , Rita Simões 1 , Fokie Cnossen 2 , Christian Jamtheim Gustafsson 3,4 , Sanne Conijn 1 , Alice Couwenberg 1 , Suzan Gerrets - van Noord 1 , Inge de Jong 1 , Vivian van Pelt 1 , Lisa Wiersema 1 , Joëlle van Aalst 5 , Jan-Jakob Sonke 1 , Charlotte L. Brouwer 5 , Tomas Janssen 1
Figure 1. Example of manual, SAM2 and refined SAM2 contours compared to ground truth. Results: Table 1 shows that SAM2 with refinement (DSC=0.854) slightly outperforms manual contouring (DSC=0.849). Using SAM2 as a pre-annotation tool reduces total annotation time by 11.35% (from 65.88 to 58.40
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