Leveraging anatomical priors for automated pancreas segmentation on abdominal CT

2025-04-04

Medical Imaging 2025: Computer-Aided Diagnosis

AI Summary

Automatic pancreatic segmentation in abdominal CT suffers from low accuracy and high false-negative rates. Method: We propose a 3D full-resolution nnU-Net framework integrated with anatomical prior knowledge—specifically, multi-organ anatomical labels from TotalSegmentator, leveraging spatial constraints from neighboring organs as strong priors, and jointly trained end-to-end on the PANORAMA dataset. Contribution/Results: Our method significantly improves segmentation robustness: Dice score increases by 6.0% (p < 0.001), Hausdorff distance decreases by 36.5 mm (p < 0.001), and achieves 100% pancreatic detection with zero false negatives. This work empirically validates the critical value of anatomical priors for fine-grained single-organ segmentation, establishing a highly reliable foundation for radiomic biomarker extraction and pancreatic lesion identification.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Improving pancreas segmentation accuracy on CT scans
• Enhancing segmentation using anatomical prior knowledge
• Reducing failed detections in pancreas identification

Innovation

• Utilizing anatomical priors for pancreas segmentation
• Training 3D nnU-Net models with refined labels
• Combining PANORAMA and TotalSegmentator dataset labels