Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT

• URL: http://arxiv.org/abs/2504.06921v1
• Date: Wed, 09 Apr 2025 14:29:08 GMT
• Title: Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
• Authors: Anisa V. Prasad, Tejas Sudharshan Mathai, Pritam Mukherjee, Jianfei Liu, Ronald M. Summers,
• Abstract summary: In this article, we investigate the utility of anatomical priors to enhance the segmentation performance of the pancreas.<n>The addition of anatomical priors resulted in a 6% increase in Dice score and a 36.5 mm decrease in Hausdorff distance for pancreas segmentation.<n>The use of anatomy priors shows promise for pancreas segmentation and subsequent derivation of imaging biomarkers.
• Score: 2.630977501633263
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior research on pancreas segmentation has primarily focused on modifying the segmentation model architecture or utilizing pre- and post-processing techniques. In this article, we investigate the utility of anatomical priors to enhance the segmentation performance of the pancreas. Two 3D full-resolution nnU-Net models were trained, one with 8 refined labels from the public PANORAMA dataset, and another that combined them with labels derived from the public TotalSegmentator (TS) tool. The addition of anatomical priors resulted in a 6\% increase in Dice score ($p < .001$) and a 36.5 mm decrease in Hausdorff distance for pancreas segmentation ($p < .001$). Moreover, the pancreas was always detected when anatomy priors were used, whereas there were 8 instances of failed detections without their use. The use of anatomy priors shows promise for pancreas segmentation and subsequent derivation of imaging biomarkers.

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