用MRI实现缺血性中风血管闭塞定位，提升诊断速度与准确性。
Stroke Locus Net: Occluded Vessel Localization from MRI Modalities
- 融合nnUNet分割与动脉图谱，端到端定位闭塞血管。
- 通过pGAN生成MRA图像辅助血管识别，定位准确率显著提升。
- 适合临床医生快速诊断中风，尤其适用于无MRA设备的场景。
缺血性中风诊断中，准确识别闭塞血管是关键挑战。现有机器学习方法多聚焦于病灶分割，对血管定位研究较少。本文提出Stroke Locus Net，一种仅使用MRI扫描的端到端深度学习框架，用于检测、分割和定位闭塞血管。系统包含两个分支：一个基于nnUNet的分割分支用于病灶检测，结合动脉图谱实现血管映射与识别；另一个基于pGAN的生成分支，可从T1 MRI合成MRA图像。实验表明，该方法在中风患者的T1 MRI上实现了对闭塞血管的有效定位，具备加速并优化中风诊断的潜力。
原文摘要 · Abstract (English)
A key challenge in ischemic stroke diagnosis using medical imaging is the accurate localization of the occluded vessel. Current machine learning methods in focus primarily on lesion segmentation, with limited work on vessel localization. In this study, we introduce Stroke Locus Net, an end-to-end deep learning pipeline for detection, segmentation, and occluded vessel localization using only MRI scans. The proposed system combines a segmentation branch using nnUNet for lesion detection with an arterial atlas for vessel mapping and identification, and a generation branch using pGAN to synthesize MRA images from MRI. Our implementation demonstrates promising results in localizing occluded vessels on stroke-affected T1 MRI scans, with potential for faster and more informed stroke diagnosis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。