Wrist bone segmentation in X-ray images using CT-based simulations

TL;DR

This paper presents a deep learning approach for wrist bone segmentation in X-ray images trained on simulated X-ray data generated from CT scans, addressing the challenge of limited annotated real data.

Contribution

It introduces a novel use of CT-based simulated X-ray images to train deep learning models for wrist bone segmentation, reducing reliance on extensive annotated datasets.

Findings

Achieved Dice scores of 0.80 to 0.92 on simulated data

Demonstrated superior qualitative segmentation on real X-ray images

Provided open access to the trained model and simulation code

Abstract

Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image segmentation is an essential step for many computer-aided diagnostic systems, yet it remains challenging. Deep-learning-based methods have achieved superior performance in medical image segmentation tasks but often require a large amount of high-quality annotated data for model training. Providing such an annotated dataset is not only time-consuming but also requires a high level of expertise. This is particularly challenging in wrist bone segmentation in X-rays, due to the interposition of multiple small carpal bones in the image. To overcome the data annotation issue, this work utilizes a large number of simulated X-ray images generated from Computed Tomography (CT) volumes with their corresponding 10 bone labels to train a deep learning-based model for wrist bone segmentation in real X-ray images. The proposed method was evaluated using both simulated images and real images. The method achieved Dice scores ranging from 0.80 to 0.92 for the simulated dataset generated from different view angles. Qualitative analysis of the segmentation results of the real X-ray images also demonstrated the superior performance of the trained model. The trained model and X-ray simulation code are freely available for research purposes: the link will be provided upon acceptance.