Erasmus MC
Radiology
Ruisheng Su is currently a PhD student at Erasmus MC, Rotterdam. His current research focuses on developing intelligent algorithms for quantitative and automatic stroke image analysis and treatment outcome assessment. His research interests include deep learning, computer vision and stroke imaging.

Presenter Of 1 Presentation

DEEP LEARNING-BASED INTRACRANIAL PERFORATION DETECTION IN DSA IMAGES OBTAINED DURING ENDOVASCULAR THROMBECTOMY

Session Type
Oral Presentations
Date
27.10.2021, Wednesday
Session Time
08:00 - 08:30
Room
ORAL PRESENTATIONS 2
Lecture Time
08:00 - 08:10

Abstract

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.

Methods

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.

Results

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.

Conclusions

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.

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