Adapting segment anything model for 3D pancreas tumor segmentation

Fan Yang; Bo Wang

doi:10.1117/12.3035136

19 July 2024 Adapting segment anything model for 3D pancreas tumor segmentation

Fan Yang, Bo Wang

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133L (2024) https://doi.org/10.1117/12.3035136
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

The Segment Anything Model (SAM) stands as a foundational model for general-purpose image segmentation, showing promising performance in various natural image segmentation tasks. However, its inherent 2D characteristics often lead to inadequacies in capturing and integrating crucial information in 3D space, particularly when dealing with volumetric medical image data. To address this problem, we propose 3D ASAM, a novel approach for volumetric medical image segmentation. Specifically, our model feeds volumetric images directly into the SAM and CNN branches to help the model better understand the complex structure and texture of the images. We introduce a series of innovative 3D adapters in the Transformer block of the image encoder, while retaining most of the pre-trained weights in SAM. We utilize a parametrically efficient fine-tuning strategy and propose a new boundary difference loss function, aiming at improving segmentation accuracy and boundaries for better medical image segmentation. We conducted extensive experiments on pancreas tumor medical image datasets, and our method achieved competitive results compared with other advanced 3D methods when only single-point cues were provided.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Yang and Bo Wang "Adapting segment anything model for 3D pancreas tumor segmentation", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133L (19 July 2024); https://doi.org/10.1117/12.3035136

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