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DEEP LEARNING-BASED INTRACRANIAL PERFORATION DETECTION IN DSA IMAGES OBTAINED DURING ENDOVASCULAR THROMBECTOMY
Background and Aims
Intracranial vessel perforation is a procedural complication during endovascular thrombectomy (EVT). Its occurrence is strongly associated with unfavorable treatment outcomes. Early identification of perforation would allow therapeutic actions to prevent situation deterioration. Due to its low occurrence and the large heterogeneity in image appearance, perforations may initially be missed by the interventionalists. In this work, we study the feasibility of automated intracranial vessel perforation detection during EVT using deep learning techniques.
From the MR CLEAN registry, fifty-three patients (149 acquisitions) with vessel perforations were identified and annotated by an experienced neuroradiologist. Another 150 acquisitions (from 150 patients) without perforations were randomly selected as negative samples. The proposed solution builds on top of state-of-the-art object detection algorithms. It incorporates temporal information of DSA with bidirectional convolutional gated recurrent units (Bi-ConvGRU), further followed by a problem-tailored acquisition level optimization to reduce false positives based on temporal consistency.
In ten-fold cross-validation on 203 patients (299 acquisitions, 3607 images), the proposed method achieves an area under the receiver-operating characteristic curve of 90% for acquisition-level classification of acquisitions with a perforation. The sensitivity and specificity were 81% and 86%, respectively.
The proposed deep learning-based algorithm achieves promising performance in vessel perforation detection in DSA for stroke patients, and can potentially be deployed in clinical practice to detect perforation early and allow direct clinical decision making.