用解剖先验约束模型，提升腹部主动脉瘤分割准确率
Anatomy-Informed Deep Learning for Abdominal Aortic Aneurysm Segmentation
- 引入器官排除掩码作为解剖先验，指导网络聚焦主动脉区域
- 在小数据集上显著降低假阳性，边界一致性提升明显
- 适合医学图像分割中数据少、结构复杂场景使用
在CT血管造影中，由于解剖变异大、血管边界对比度低以及邻近器官与血管强度相似，腹主动脉瘤（AAA）的精确分割极具挑战，常导致假阳性。为此，我们提出一种解剖感知分割框架，将TotalSegmentator生成的器官排除掩码融入训练过程。这些掩码通过识别非血管器官并惩罚其区域内的动脉瘤预测，明确引入解剖先验，引导U-Net专注于主动脉及其病理性扩张，同时抑制解剖上不合理的预测。尽管训练数据量有限，该方法仍实现高精度，显著减少假阳性，并提升边界一致性，相比标准U-Net基线表现更优。结果表明，通过排除掩码融入解剖知识，是一种高效提升模型鲁棒性与泛化能力的机制，可在数据有限情况下实现可靠的AAA分割。
原文摘要 · Abstract (English)
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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