Towards clinical translation of deep-learning based classification of
  DSA image sequences for stroke treatment
Timo Baumgärtner Benjamin J. Mittmann Till Malzacher Johannes Roßkopf Michael Braun Bernd Schmitz Alfred M. Franz
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.