CACCT: An Automated Tool of Detecting Complicated Cardiac Malformations in Mouse Models.
Qing ChuHaobin JiangLibo ZhangDekun ZhuQianqian YinHao ZhangBin ZhouWenzhang ZhouZhang YueHong LianLihui LiuNie YuShengshou HuPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2020)
Congenital heart disease (CHD) is the major cause of morbidity/mortality in infancy and childhood. Using a mouse model to uncover the mechanism of CHD is essential to understand its pathogenesis. However, conventional 2D phenotyping methods cannot comprehensively exhibit and accurately distinguish various 3D cardiac malformations for the complicated structure of heart cavity. Here, a new automated tool based on microcomputed tomography (micro-CT) image data sets known as computer-assisted cardiac cavity tracking (CACCT) is presented, which can detect the connections between cardiac cavities and identify complicated cardiac malformations in mouse hearts automatically. With CACCT, researchers, even those without expert training or diagnostic experience of CHD, can identify complicated cardiac malformations in mice conveniently and precisely, including transposition of the great arteries, double-outlet right ventricle and atypical ventricular septal defect, whose accuracy is equivalent to senior fetal cardiologists. CACCT provides an effective approach to accurately identify heterogeneous cardiac malformations, which will facilitate the mechanistic studies into CHD and heart development.
Keyphrases
- left ventricular
- mouse model
- congenital heart disease
- heart failure
- machine learning
- computed tomography
- type diabetes
- high throughput
- body mass index
- atrial fibrillation
- skeletal muscle
- electronic health record
- mitral valve
- pulmonary hypertension
- cardiovascular events
- pulmonary arterial hypertension
- coronary artery disease
- single cell
- artificial intelligence
- positron emission tomography
- image quality