INSTED2024

任务是从健康志愿者和颅内动脉狭窄或颅内动脉瘤患者组成的队列获取的 3D TOF-MRA 图像中检测和分割病变。精确的病变检测和分割将有助于狭窄和颅内动脉瘤的识别和定量表征，这在临床实践中具有重要意义。

The task is to detect and segment lesions from 3D TOF-MRA images acquired from a cohort consisting of healthy volunteers and patients with intracranial arterial stenosis or intracranial aneurysms. Accurate lesion detection and segmentation will facilitate the identification and quantitative characterization of intracranial arterial stenosis and intracranial aneurysms, which is of great significance in clinical practice.

搜集汇总

背景与挑战

背景概述

INSTED2024是一个专注于颅内动脉瘤和颅内动脉狭窄检测与分割的医学影像数据集，包含160个3D TOF-MRA图像案例，涵盖健康、动脉瘤和狭窄三类数据，并提供病变的边界框和分割掩码注释。该数据集旨在支持自动检测和分割算法的开发，用于脑血管疾病的定量分析，具有临床筛查和研究价值。