Modality agnostic intracranial aneurysm detection through supervised vascular surface classification

Žiga Bizjak; Boštjan Likar; Franjo Pernuš; Žiga Špiclin

doi:10.1117/12.2580868

15 February 2021 Modality agnostic intracranial aneurysm detection through supervised vascular surface classification

Žiga Bizjak, Boštjan Likar, Franjo Pernuš, Žiga Špiclin

Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115970O (2021) https://doi.org/10.1117/12.2580868
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Intracranial aneurysms (IAs) are mostly asymptomatic and thus often discovered incidentally on angiographic scans like 3D DSA, CTA and MRA. Skilled radiologists achieved a sensitivity of 88% by means of visual detection, which seems inadequate considering that prevalence of IAs in general population is 3-5%. Deep learning models trained and executed on angiographic scans seem best-suited for IA detection, however, reported performances across different modalities is currently insufficient for clinical application. This paper presents a novel modality agnostic method for detection of IAs. First the triangulated surfaces of vascular structures were roughly extracted from the angiograms. For IA detection purpose, the extracted surfaces were randomly parcellated into local patches and then a translation, rotation and scale invariant classifier based on deep neural network (DNN) was trained. Test stage proceeded by mimicking the surface extraction and parcellation at several random locations, then the trained DNN model was applied for classification, and the results aggregated into IA detection heatmaps across entire vascular surface. For training and validation the extracted contours were presented to skilled neurosurgeon, who marked the locations of IAs. The DNN was trained and tested using three-fold cross-validation based on 57 DSAs, 5 CTAs and 5 MRAs and showed a 98.6% sensitivity at 0.2 false positive detections per image. Experimental results show that proposed approach not only significantly improved detection sensitivity and specificity compared to state-of-the-art intensity based methods, but is also modality agnostic and thus better suited for clinical application.

Conference Presentation

Citation Download Citation

Žiga Bizjak, Boštjan Likar, Franjo Pernuš, and Žiga Špiclin "Modality agnostic intracranial aneurysm detection through supervised vascular surface classification", Proc. SPIE 11597, Medical Imaging 2021: Computer-Aided Diagnosis, 115970O (15 February 2021); https://doi.org/10.1117/12.2580868

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