In the event of stroke, a catheter-guided procedure (thrombectomy) is used to
remove blood clots. Feasibility of machine learning based automatic
classifications for thrombus detection on digital substraction angiography
(DSA) sequences has been demonstrated. It was however not used live in the
clinic, yet. We present an open-source tool for automatic thrombus
classification and test it on three selected clinical cases regarding
functionality and classification runtime. With our trained model all large
vessel occlusions in the M1 segment were correctly classified. One small
remaining M3 thrombus was not detected. Runtime was in the range from 1 to 10
seconds depending on the used hardware. We conclude that our open-source
software tool enables clinical staff to classify DSA sequences in (close to)
realtime and can be used for further studies in clinics.

本文介绍了一种开源工具，采用机器学习技术实现数字减影血管造影（DSA）血栓检测的自动分类，并在三个临床病例中进行了测试，其模型能够正确分类 M1 分段中的所有大血管闭塞，并可以在 (接近) 实时的情况下被临床工作人员使用。