用AI自动识别子宫内膜异位症患者的周围神经，提升影像诊断精度。
Visionerves: Automatic and Reproducible Hybrid AI for Peripheral Nervous System Recognition Applied to Endometriosis Cases
- 融合深度学习与符号推理，自动分割解剖结构并识别神经路径。
- 相比传统方法，神经识别准确率提升25%，定位误差小于5毫米。
- 适合临床神经影像分析、子宫内膜异位症研究及非侵入性诊断应用。
子宫内膜异位症常引发慢性盆腔痛并累及周围神经，但神经影像仍具挑战。本文提出Visionerves，一种新型混合AI框架，基于多梯度DWI与形态学MRI数据实现周围神经系统识别。不同于传统纤维追踪，Visionerves通过模糊空间关系编码解剖知识，无需手动勾画ROI。该流程分两阶段：(A) 使用深度学习模型自动分割解剖结构；(B) 通过符号空间推理完成纤维追踪与神经识别。在10例确诊或疑似子宫内膜异位症女性的腰骶丛中应用，Visionerves相较标准追踪显著提升性能，Dice分数最高提升25%，空间误差降低至5毫米以下。该自动且可复现的方法为子宫内膜异位症相关神经病变的非侵入性诊断及其它神经受累疾病研究提供新路径。
原文摘要 · Abstract (English)
Endometriosis often leads to chronic pelvic pain and possible nerve involvement, yet imaging the peripheral nerves remains a challenge. We introduce Visionerves, a novel hybrid AI framework for peripheral nervous system recognition from multi-gradient DWI and morphological MRI data. Unlike conventional tractography, Visionerves encodes anatomical knowledge through fuzzy spatial relationships, removing the need for selection of manual ROIs. The pipeline comprises two phases: (A) automatic segmentation of anatomical structures using a deep learning model, and (B) tractography and nerve recognition by symbolic spatial reasoning. Applied to the lumbosacral plexus in 10 women with (confirmed or suspected) endometriosis, Visionerves demonstrated substantial improvements over standard tractography, with Dice score improvements of up to 25% and spatial errors reduced to less than 5 mm. This automatic and reproducible approach enables detailed nerve analysis and paves the way for non-invasive diagnosis of endometriosis-related neuropathy, as well as other conditions with nerve involvement.
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