Time Matters: Handling Spatio-Temporal Perfusion Information for Automated TICI Scoring

Abstract

X-ray digital subtraction angiography (DSA) imaging is the backbone of diagnosis and therapy response assessment in cerebral ischemic stroke. To evaluate and document the success of endovascular interventions, the spatio-temporal DSA image information and perfusion dynamics are visually assessed by a clinical expert and reperfusion rated using the so-called TICI (treatment in cerebral ischemia) score. Although clinical standard, it is well known that TICI scoring is time-consuming, observer-dependent and not practicable especially in larger clinical studies. Automated TICI scoring has, however, been considered beyond the scope of machine learning capabilities, due to the complexity of the classification task (eg. heterogeneity of clinical DSA data and a complex dependence between TICI score and perfusion dynamics). The present work describes the first study that tackles automated TICI scoring using deep spatio-temporal learning. It thereby defines the first corresponding benchmark. Methodically, we build on gated recurrent unit networks (GRUs) and integrate knowledge about the perfusion and TICI scoring process into loss functions and network training to increase prediction robustness. Differences between GRU-predicted mTICI scores and routine mTICI scores are in the order of literature-reported interrater variability of human expert-based TICI scoring.