Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908309764702208 |
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| author | Prasad, Anisa V. Mathai, Tejas Sudharshan Mukherjee, Pritam Liu, Jianfei Summers, Ronald M. |
| author_facet | Prasad, Anisa V. Mathai, Tejas Sudharshan Mukherjee, Pritam Liu, Jianfei Summers, Ronald M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT Prasad, Anisa V. Mathai, Tejas Sudharshan Mukherjee, Pritam Liu, Jianfei Summers, Ronald M. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.06921 |