Deep shape based intracranial aneurysm rupture prediction

June Ho Choi; Žiga Bizjak; Wonhyoung Park; Jannik Sobisch; Žiga Špiclin

doi:10.1117/12.3007178

3 April 2024 Deep shape based intracranial aneurysm rupture prediction

June Ho Choi, Žiga Bizjak, Wonhyoung Park, Jannik Sobisch, Žiga Špiclin

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272I (2024) https://doi.org/10.1117/12.3007178
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, an event that is often fatal. Human evaluation of IA rupture is rather subjective, therefore we aimed to develop a deep learning based rupture event prediction model from aneurysm shape information. We used 386 CTA scans with 500 IAs that were either unruptured or ruptured (250/250). The IA rupture status was computed using bottleneck layer feature vectors sourced from two deep learning models trained for the respective auxiliary tasks of cerebral vessel labeling and aneurysm isolation from its parent vessels. The two extracted feature vectors were concatenated with 20 established features (patient sex, age, aneurysm location and morphological parameters) and used to predict aneurysm rupture status using eight different machine learning models. We achieved the best AUC of 0.851 using random forest with feature selection based on Spearman’s rank correlation thresholding. The rather good performance of IA rupture status classification renders the proposed approach as a promising tool for management of rupture risk in the ”no treatment” paradigm of patient follow-up imaging.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

June Ho Choi, Žiga Bizjak, Wonhyoung Park, Jannik Sobisch, and Žiga Špiclin "Deep shape based intracranial aneurysm rupture prediction", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272I (3 April 2024); https://doi.org/10.1117/12.3007178

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