Cerebral artery segmentation again

中风是导致死亡的主要原因之一 脑卒中是一种以脑血管病变为特征的脑血管疾病，如动脉狭窄和闭塞。准确评估脑血管病变对其诊断和治疗具有重要意义。磁共振血管造影(MRA)被广泛应用于脑血管树的可视化诊断。准确的冠状动脉MRA分割对脑血管疾病的定量分析，如程度的估计具有重要意义 考虑到大脑动脉的复杂网络存在显著的个体间差异，以及小血管中的微弱信号，即使对专家来说，分割也是具有挑战性的。飞行时间IICFI MRA是最广泛使用的非介入成像技术，可以在不使用造影剂的情况下描绘脑血管树的解剖结构。然而，具有良好标记的脑动脉的可访问的大规模TOF-MRA数据是有限的，这阻碍了可靠的自动脑动脉分割算法的开发和验证。 在这个挑战中，任务是艰巨的。对有症状性颅内动脉狭窄患者的三维TOF-MRA图像进行脑动脉的精确分割，有助于狭窄的识别和定量表征，有助于动脉硬化、夹层、动脉炎等血管疾病的诊断。烟雾病、可逆性脑血管收缩综合征等。

Stroke is one of the leading causes of death. Stroke is a cerebrovascular disease characterized by cerebrovascular pathologies such as arterial stenosis and occlusion. Accurate assessment of cerebrovascular pathologies is of great significance for its diagnosis and treatment. Magnetic resonance angiography (MRA) is widely used in the diagnostic visualization of the cerebral vascular tree. Accurate cerebral MRA segmentation is of great significance for the quantitative analysis of cerebrovascular diseases, such as the estimation of stenosis severity. Given the complex network of cerebral arteries with significant inter-individual differences and weak signals in small vessels, segmentation is challenging even for experts. Time-of-Flight IICFI MRA is the most widely used non-invasive imaging technique that can depict the anatomical structure of the cerebral vascular tree without contrast agents. However, the availability of large-scale, well-annotated TOF-MRA datasets of cerebral arteries is limited, which hinders the development and validation of reliable automatic cerebral artery segmentation algorithms. The task in this challenge is arduous. Precise segmentation of cerebral arteries from three-dimensional TOF-MRA images of patients with symptomatic intracranial arterial stenosis can help identify and quantitatively characterize stenosis, and assist in the diagnosis of vascular diseases such as arteriosclerosis, arterial dissection, and arteritis, as well as Moyamoya disease, reversible cerebral vasoconstriction syndrome, etc.

搜集汇总

背景与挑战

背景概述

该数据集专注于脑动脉分割任务，旨在利用三维TOF-MRA图像对颅内动脉狭窄患者进行精确分割，以辅助脑血管疾病诊断。由于大脑动脉网络复杂且个体差异显著，加上小血管信号微弱和标记数据有限，分割工作面临挑战。