MiceBoneChallenge: Micro-CT public dataset and six solutions for automatic growth plate detection in micro-CT mice bone scans

TL;DR

This paper presents a new public dataset of micro-CT mouse bone scans and evaluates six AI solutions for automatic growth plate detection, achieving accuracy suitable for clinical use.

Contribution

It introduces a high-quality annotated micro-CT dataset and benchmarks six novel AI solutions for automatic growth plate detection in mice bones.

Findings

Six AI solutions accurately identified growth plates with mean error of 1.91 planes.

The dataset and solutions are publicly available for further research.

The solutions demonstrate practical accuracy for radiological applications.

Abstract

Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Company organized an internal challenge to develop models for automatic bone quantification. We prepared and annotated a high-quality dataset of 3D $\mu$CT bone scans from $83$ mice. The challenge attracted over $80$ AI scientists from around the globe who formed $23$ teams. The participants were tasked with developing a solution to identify the plane where the bone growth happens, which is essential for fully automatic segmentation of trabecular bone. As a result, six computer vision solutions were developed that can accurately identify the location of the growth plate plane. The solutions achieved the mean absolute error of $1.91\pm0.87$ planes from the ground truth on the test set, an accuracy level acceptable for practical use by a radiologist. The annotated 3D scans dataset along with the six solutions and source code, is being made public, providing researchers with opportunities to develop and benchmark their own approaches. The code, trained models, and the data will be shared.