Semi-automatic detection and segmentation algorithm of saccular aneurysms in 2D cerebral DSA images

Abstract—Abstract To detect and segment cerebral saccular aneurysms (CSAs) in 2D Digital
Subtraction Angiography (DSA) images.Patients and methods: Ten patients underwent Intra-arterial DSA procedures. Patients were injected with Iodine-containing radiopaque material. A scheme for semi-automatic detection and
segmentation of intracranial aneurysms is proposed in this study. The algorithm consisted of three
major image processing stages: image enhancement, image segmentation and image classification.
Applied to the 2D Digital Subtraction Angiography (DSA) images, the algorithm was evaluated
in 19 scene files to detect 10 CSAs.Results: Aneurysms were identified by the proposed detection and segmentation algorithm with 89.47% sensitivity and 80.95% positive predictive value (PPV) after executing the algorithm on
19 DSA images of 10 aneurysms. Results have been verified by specialized radiologists. However,
4 false positive aneurysms were detected when aneurysms’ location is at Anterior Communicating
Artery (ACA).Conclusion: The suggested algorithm is a promising method for detection and segmentation of saccular
aneurysms; it provides a diagnostic tool for CSAs.The Egyptian Society of Radiology and Nuclear Medicine. Production and hosting by Elsevier.This is an open access article under the CC BY-NC-ND license.