A high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction

Vascular network reconstruction is a crucial step in extracting vessel morphology and establishing its topological relationships from biological imaging data, holding significant scientific importance for studying brain structure and function, metabolism, and disease mechanisms. Current methods for vascular network reconstruction typically follow a "segment-first, then reconstruct" pipeline: first generating a binary segmentation from vascular images, followed by topological modeling. However, due to significant variations in vessel diameters, frequent presence of luminal voids in large vessels, and the complex, densely distributed nature of capillaries, existing approaches still face notable limitations in reconstruction accuracy. To address this, this study introduces and releases an annotated dataset of mouse brain vasculature. The dataset comprises 60 3D image blocks with the size of 512 × 512 × 512 acquired from four mouse brain samples using fluorescence micro-optical sectioning tomography (fMOST) imaging. It encompasses diverse structural morphologies ranging from large vessels to capillaries, with detailed annotations specifically targeting challenging vascular regions. Additionally, we provide a standardized vascular annotation pipeline and associated tools. This dataset aims to serve as a benchmark to support the development, evaluation, and comparison of algorithms for vascular network segmentation, reconstruction, and related tasks.