The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla MRI systems has enabled the acquisition of higher spatial resolution images, making it possible to visualise such vessels in the brain. However, the lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. To address this, the SMILE-UHURA challenge was organised. This challenge, held in conjunction with the ISBI 2023, in Cartagena de Indias, Colombia, aimed to provide a platform for researchers working on related topics. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. This dataset was created through a combination of automated pre-segmentation and extensive manual refinement. In this manuscript, sixteen submitted methods and two baseline methods are compared both quantitatively and qualitatively on two different datasets: held-out test MRAs from the same dataset as the training data (with labels kept secret) and a separate 7T ToF MRA dataset where both input volumes and labels are kept secret. The results demonstrate that most of the submitted deep learning methods, trained on the provided training dataset, achieved reliable segmentation performance. Dice scores reached up to 0.838 $\pm$ 0.066 and 0.716 $\pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $\pm$ 0.15.

翻译：人脑通过错综复杂的血管网络获取营养与氧气。介观尺度上影响小血管的病变是脑部供血系统的关键脆弱环节，可导致脑小血管疾病等严重病症。7特斯拉磁共振成像系统的出现，使获取更高空间分辨率的图像成为可能，进而能够观察脑部此类血管。然而，公开标注数据集的匮乏阻碍了稳健的机器学习驱动分割算法的发展。为此，我们组织了SMILE-UHURA挑战赛。该挑战赛与2023年哥伦比亚卡塔赫纳举办的ISBI会议同期举行，旨在为相关领域研究者提供交流平台。SMILE-UHURA挑战赛通过提供基于7T MRI采集的时间飞跃法血管造影标注数据集，填补了公开标注数据集的空白。该数据集通过自动化预分割与大量人工修正相结合的方式构建。本文对提交的16种方法和2种基线方法在两个不同数据集上进行了定量与定性比较：其一为与训练数据同源但标注保密的测试MRA数据集，其二为输入图像与标注均保密的独立7T ToF MRA数据集。结果表明，基于所提供的训练数据集训练的深度学习方法大多实现了可靠的分割性能。在两个数据集上的Dice系数分别达到最高0.838±0.066和0.716±0.125，平均性能达0.804±0.15。