StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm

Document Type

Article

Publication Date

9-1-2023

Publication Title

Computerized medical imaging and graphics

Abstract

Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.

Medical Subject Headings

Humans; Intracranial Aneurysm; Aneurysm, Ruptured; Neural Networks, Computer; Angiography, Digital Subtraction

PubMed ID

37556901

Volume

108

First Page

102271

Last Page

102271

Find It @ Sladen

Share

COinS