The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations.

翻译：神经元间的布线和连接构成了神经系统功能的结构基础。体积电子显微镜（EM）和图像分割技术的进步已实现小鼠大脑局部区域回路图谱（连接组学）的绘制。然而，由于技术挑战，目前尚无法对整个大脑进行体积EM成像，导致大脑区域间长程连接的综合图谱缺失。近期，我们证明X射线全息纳米断层扫描（XNH）能以远超EM的规模提供脑组织高分辨率图像。特别地，XNH特别适合解析构成长程连接（投射）主体的大型有髓轴突束（白质），这些结构对区域间通信至关重要。因此，XNH为全脑投射组学提供了成像解决方案。然而，由于XNH数据通常以低于EM的分辨率和更大的视野采集，准确分割XNH图像仍是重要挑战，本文即聚焦于此。本任务中，我们提供小鼠大脑皮层白质轴突的体积XNH图像及其轴突轨迹的真值标注。手动体素级真值标注是训练分割网络的耗时瓶颈。相比之下，基于骨架的真值标注速度更快，且足以确定连通性。因此，我们鼓励参与者开发利用骨架训练的方法。为此，我们提供两种真值标注：小体积体素级标注和更大体积的骨架标注。参赛结果将依据提交的分割结果与骨架真值标注的吻合精度进行评估。