# Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT

arXiv: https://arxiv.org/abs/2504.06921 · HTML: https://mlsanity.com/paper/2504.06921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)
- [The Importance of Skip Connections in Biomedical Image Segmentation](https://mlsanity.com/paper/1608.04117/) (2016)
- [Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation](https://mlsanity.com/paper/1903.03148/) (2019)
- [MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI](https://mlsanity.com/paper/2405.05944/) (2024)
- [Hierarchical 3D Feature Learning for Pancreas Segmentation](https://mlsanity.com/paper/2109.01667/) (2021)
- [A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images](https://mlsanity.com/paper/2206.03603/) (2022)
- [Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors](https://mlsanity.com/paper/2401.06272/) (2024)
- [Weakly-Supervised Detection of Bone Lesions in CT](https://mlsanity.com/paper/2402.00175/) (2024)

## Built on by (most cited first, 1 of 1)

- [Utility of Pancreas Surface Lobularity as a CT Biomarker for Opportunistic Screening of Type 2 Diabetes](https://mlsanity.com/paper/2511.10484/) (2025)

## Abstract (arXiv)

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.