Abstract
Accurate pancreas segmentation is crucial for diagnosing and managing pancreatic diseases, facilitating preoperative planning, and aiding transplantation procedures. Effective segmentation enables the identification and monitoring of conditions such as chronic pancreatitis and diabetes mellitus, which are characterized by changes in pancreatic size and volume. Recent advancements in segmentation technology have leveraged foundation models like SAM and MedSAM, achieving state-of-the-art performance in various domains. In this work, we explore the effectiveness of using these models in the particularly challenging domain of pancreas segmentation. We also propose a simple yet effective method for including 3D information into SAM. Our findings suggest that, despite foundation models have a good general knowledge, they are not well-suited for pancreas segmentation without significant architectural modifications and the inclusion of a good prompt. Moreover, we found that simply including volume information significantly enhances segmentation performance, even without the use of an expert prompt.
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S. Calcagno acknowledges financial support from PNRR MUR project PE0000013-FAIR.
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Rapisarda, E., Gravagno, A.G., Calcagno, S., Giordano, D. (2025). Assessing the Efficacy of Foundation Models in Pancreas Segmentation. In: Proietto Salanitri, F., et al. Artificial Intelligence in Pancreatic Disease Detection and Diagnosis, and Personalized Incremental Learning in Medicine. PILM AIPAD 2024 2024. Lecture Notes in Computer Science, vol 15197. Springer, Cham. https://doi.org/10.1007/978-3-031-73483-0_1
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DOI: https://doi.org/10.1007/978-3-031-73483-0_1
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