  Nonunion is one of the challenges faced by orthopedics clinics for the
technical difficulties and high costs in photographing interosseous
capillaries. Segmenting vessels and filling capillaries are critical in
understanding the obstacles encountered in capillary growth. However, existing
datasets for blood vessel segmentation mainly focus on the large blood vessels
of the body, and the lack of labeled capillary image datasets greatly limits
the methodological development and applications of vessel segmentation and
capillary filling. Here, we present a benchmark dataset, named IFCIS-155,
consisting of 155 2D capillary images with segmentation boundaries and vessel
fillings annotated by biomedical experts, and 19 large-scale, high-resolution
3D capillary images. To obtain better images of interosseous capillaries, we
leverage state-of-the-art immunofluorescence imaging techniques to highlight
the rich vascular morphology of interosseous capillaries. We conduct
comprehensive experiments to verify the effectiveness of the dataset and the
benchmarking deep learning models (\eg UNet/UNet++ and the modified
UNet/UNet++). Our work offers a benchmark dataset for training deep learning
models for capillary image segmentation and provides a potential tool for
future capillary research. The IFCIS-155 dataset and code are all publicly
available at \url{https://github.com/ncclabsustech/IFCIS-55}.
