Most of the intracranial aneurysms (ICA) occur on a specific portion of the
cerebral vascular tree named the Circle of Willis (CoW). More particularly,
they mainly arise onto fifteen of the major arterial bifurcations constituting
this circular structure. Hence, for an efficient and timely diagnosis it is
critical to develop some methods being able to accurately recognize each
Bifurcation of Interest (BoI). Indeed, an automatic extraction of the
bifurcations presenting the higher risk of developing an ICA would offer the
neuroradiologists a quick glance at the most alarming areas. Due to the recent
efforts on Artificial Intelligence, Deep Learning turned out to be the best
performing technology for many pattern recognition tasks. Moreover, various
methods have been particularly designed for medical image analysis purposes.
This study intends to assist the neuroradiologists to promptly locate any
bifurcation presenting a high risk of ICA occurrence. It can be seen as a
Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates
the access to the regions of interest within the MRI. In this work, we propose
a method for a fully automatic detection and recognition of the bifurcations of
interest forming the Circle of Willis. Several neural networks architectures
have been tested, and we thoroughly evaluate the bifurcation recognition rate.

本研究旨在辅助神经放射科医生迅速定位任何具有 ICA 发生高风险的分叉点，提出了一种全自动检测和识别希尔脑血管环所形成的分叉点的方法，并对分叉点识别率进行了全面评估。

基于深度学习的自动检测和分类 Circle of Willis 血管分叉

Using deep learning for an automatic detection and classification of the  vascular bifurcations along the Circle of Willis

Intracranial aneurysms are a commonly occurring and life-threatening
condition, affecting approximately 3.2% of the general population.
Consequently, detecting these aneurysms plays a crucial role in their
management. Lesion detection involves the simultaneous localization and
categorization of abnormalities within medical images. In this study, we
employed the nnDetection framework, a self-configuring framework specifically
designed for 3D medical object detection, to detect and localize the 3D
coordinates of aneurysms effectively. To capture and extract diverse features
associated with aneurysms, we utilized TOF-MRA and structural MRI, both
obtained from the ADAM dataset. The performance of our proposed deep learning
model was assessed through the utilization of free-response receiver operative
characteristics for evaluation purposes. The model's weights and 3D prediction
of the bounding box of TOF-MRA are publicly available at
this https URL

使用 nnDetection 框架和 TOF-MRA 进行医学图像分析，能够有效检测和定位颅内动脉瘤，具有潜在的临床应用。