Aims-Tbi2024

这项挑战将专注于识别T1加权MRI数据中的病变，因为它是我们ENIGMA TBI联盟中最常见的MRI扫描。病灶分割的进展和精确病灶掩膜的实现将使病灶分割进入下一个图像处理和分析（如分区、功能连接分析、连接组学、基于固定位的分析），从而实现更准确的预后，并可能改善患者的长期预后。

This challenge focuses on identifying lesions in T1-weighted MRI data, as this imaging modality is the most common MRI scan utilized in our ENIGMA TBI Consortium. Advances in lesion segmentation and the generation of precise lesion masks will enable next-generation image processing and analytical workflows, including parcellation, functional connectivity analysis, connectomics, and position-based analysis, thereby facilitating more accurate prognostication and potentially improving long-term patient outcomes.