Relationship between Circle of Willis Variations and Cerebral or Cervical Arteries Stenosis Investigated by Computer Tomography Angiography and Multitask Convolutional Neural Network

Abstract

Circle of Willis (CoW) is the most critical collateral pathway that supports the redistribution of blood supply in the brain. The variation of CoW is closely correlated with cerebral hemodynamic and cerebral vessel-related diseases. But what is responsible for CoW variation remains unclear. Moreover, the visual evaluation for CoW variation is highly time-consuming. In the present study, based on the computer tomography angiography (CTA) dataset from 255 patients, the correlation between the CoW variations with age, gender, and cerebral or cervical artery stenosis was investigated. A multitask convolutional neural network (CNN) was used to segment cerebral arteries automatically. The results showed the prevalence of variation of the anterior communicating artery (Aco) was higher in the normal senior group than in the normal young group and in females than in males. The changes in the prevalence of variations of individual segments were not demonstrated in the population with stenosis of the afferent and efferent arteries, so the critical factors for variation are related to genetic or physiological factors rather than pathological lesions. Using the multitask CNN model, complete cerebral and cervical arteries could be segmented and reconstructed in 120 seconds, and an average Dice coefficient of 78.2% was achieved. The segmentation accuracy for precommunicating part of anterior cerebral artery and posterior cerebral artery, the posterior communicating arteries, and Aco in CoW was 100%, 99.2%, 94%, and 69%, respectively. Artificial intelligence (AI) can be considered as an adjunct tool for detecting the CoW, particularly related to reducing workload and improving the accuracy of the visual evaluation. The study will serve as a basis for the following research to determine an individual’s risk of stroke with the aid of AI.

Machine Learning Approaches for Segmentation of Cardiovascular Neurocristopathy Related Images

“…Table 4 summarizes the reviewed literature that performed CT image segmentation using different DL tools. Only two studies, [86] and [62], reported the availability/sharing of the CT image datasets on request.…”

“…The review also revealed that there is limited access to publicly available open-access datasets. Only 1 out of 32 studies [58] reported that their data set was open access, while 6 out of 32 studies [60]- [62], [78], [86], [97] reported that the data is available to researchers on reasonable request. The limited availability of open-access medical data is subject to privacy and data security.…”

Topology‐aware anatomical segmentation of the Circle of Willis: HUNet unveils the vascular network

“…Various imaging techniques, such as Computed Tomography Angiography (CTA) and Magnetic Resonance Angiography (MRA), are employed to visualize and analyze the CoW and associated blood vessels [13–16]. CTA uses X‐rays and contrast agents to create detailed three‐dimensional images.…”