Source of Publication
IAES International Journal of Artificial Intelligence
Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography (MRA) images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.
Computer Sciences | Medicine and Health Sciences
Cerebrovascular, CNN, MRA, Segmentation, U-Net
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Taher, Fatma and Prakash, Neema, "Automatic cerebrovascular segmentation methods - a review" (2021). All Works. 4306.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series