PanSAM: Zero-Shot, Prompt-Free Pancreas Segmentation in CT Imaging

Segmentation of the pancreas in CT images is crucial in multiple pancreatic diagnostic tasks, such as the detection, classification, and prognosis of pancreatic cancer. We present a segmentation model to find pancreatic tissue accurately in abdominal CT images. We utilize the Segment-Anything Model (SAM), a prompt-based 2D segmentation transformer model, and adapt it to 3D CT images to build a model that can segment the pancreas automatically without any prompts. To our knowledge, this is the first prompt-free work to segment the pancreas on a CT image based on the generalizable SAM model. We achieve a DICE score of 87.01\% and a Jaccard score of 81.42\% on the NIH dataset. We also performed zero-shot segmentation on the Abdominal-1K dataset. We achieved a DICE score of 83.20\%, which shows the generalizability and applicability of our method to new unseen samples. Our study put together the zero-shot performance of SAM and the 3D nature of CT images to provide an automatic, real-time model that provides consistent segmentation throughout CT slices without the need for expert intervention. Our code is available at:https://github.com/teimuri/PSDDSAM