Immunofluorescence Capillary Imaging Segmentation: Cases Study

TL;DR

This paper introduces a new benchmark dataset of 2D and 3D immunofluorescence capillary images with expert annotations, enabling improved deep learning segmentation methods for interosseous capillaries.

Contribution

It provides the first comprehensive dataset of labeled capillary images and evaluates deep learning models, advancing research in capillary segmentation and analysis.

Findings

Deep learning models like UNet outperform traditional methods.

The dataset improves the training and benchmarking of capillary segmentation.

Immunofluorescence imaging enhances visualization of interosseous capillaries.

Abstract

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}.