ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease

TL;DR

This paper introduces ImageCHD, a pioneering 3D CT image dataset for congenital heart disease classification, highlighting the challenges and providing a baseline for future machine learning research in this domain.

Contribution

The paper presents the first publicly available 3D CT dataset for CHD classification and offers a baseline framework demonstrating current limitations and potential for future improvements.

Findings

Baseline accuracy of 82.0% on CHD classification

Dataset contains 110 diverse 3D CT images

Highlights the difficulty of structural change detection in limited data

Abstract

Congenital heart disease (CHD) is the most common type of birth defect, which occurs 1 in every 110 births in the United States. CHD usually comes with severe variations in heart structure and great artery connections that can be classified into many types. Thus highly specialized domain knowledge and the time-consuming human process is needed to analyze the associated medical images. On the other hand, due to the complexity of CHD and the lack of dataset, little has been explored on the automatic diagnosis (classification) of CHDs. In this paper, we present ImageCHD, the first medical image dataset for CHD classification. ImageCHD contains 110 3D Computed Tomography (CT) images covering most types of CHD, which is of decent size Classification of CHDs requires the identification of large structural changes without any local tissue changes, with limited data. It is an example of a larger class of problems that are quite difficult for current machine-learning-based vision methods to solve. To demonstrate this, we further present a baseline framework for the automatic classification of CHD, based on a state-of-the-art CHD segmentation method. Experimental results show that the baseline framework can only achieve a classification accuracy of 82.0\% under a selective prediction scheme with 88.4\% coverage, leaving big room for further improvement. We hope that ImageCHD can stimulate further research and lead to innovative and generic solutions that would have an impact in multiple domains. Our dataset is released to the public compared with existing medical imaging datasets.