Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation

• URL: http://arxiv.org/abs/2604.10312v1
• Date: Sat, 11 Apr 2026 18:27:35 GMT
• Title: Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation
• Abstract summary: We propose an anatomy-aware segmentation framework that integrates organ exclusion masks into the training process.<n>These masks encode explicit anatomical priors by identifying non-vascular organs and penalizing aneurysm predictions within these regions.<n>Despite being trained on a relatively small dataset, the anatomy-aware model achieves anatomically high accuracy, substantially reduces false positives, and improves boundary consistency compared to a standard U-Net baseline.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In CT angiography, the accurate segmentation of abdominal aortic aneurysms (AAAs) is difficult due to large anatomical variability, low-contrast vessel boundaries, and the close proximity of organs whose intensities resemble vascular structures, often leading to false positives. To address these challenges, we propose an anatomy-aware segmentation framework that integrates organ exclusion masks derived from TotalSegmentator into the training process. These masks encode explicit anatomical priors by identifying non-vascular organsand penalizing aneurysm predictions within these regions, thereby guiding the U-Net to focus on the aorta and its pathological dilation while suppressing anatomically implausible predictions. Despite being trained on a relatively small dataset, the anatomy-aware model achieves high accuracy, substantially reduces false positives, and improves boundary consistency compared to a standard U-Net baseline. The results demonstrate that incorporating anatomical knowledge through exclusion masks provides an efficient mechanism to enhance robustness and generalization, enabling reliable AAA segmentation even with limited training data.

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