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：人脑通过一个错综复杂的血管网络获取营养和氧气。在介观尺度上影响小血管的病理变化，是脑血液供应中的一个关键脆弱环节，可能导致严重的疾病，例如脑小血管病。7特斯拉MRI系统的出现使得获取更高空间分辨率的图像成为可能，从而能够可视化脑内的此类血管。然而，公开可用的标注数据集的缺乏，阻碍了鲁棒的、机器学习驱动的分割算法的发展。为解决此问题，SMILE-UHURA挑战赛得以组织。该挑战赛与在哥伦比亚卡塔赫纳举行的ISBI 2023会议联合举办，旨在为从事相关主题研究的人员提供一个平台。SMILE-UHURA挑战赛通过提供一个使用7T MRI采集的飞行时间血管造影标注数据集，来弥补公开可用标注数据集的不足。该数据集是通过自动预分割和大量人工精细化相结合的方式创建的。在本手稿中，我们在两个不同的数据集上，对十六种提交的方法和两种基线方法进行了定量和定性比较：一个是从与训练数据相同的数据集中保留的测试MRA（其标签保密），另一个是独立的7T ToF MRA数据集，其输入体积和标签均保密。结果表明，大多数提交的深度学习方法，在提供的训练数据集上进行训练后，都取得了可靠的分割性能。在两个数据集上的Dice分数分别高达0.838 $\pm$ 0.066和0.716 $\pm$ 0.125，平均性能高达0.804 $\pm$ 0.15。