A deep learning pipeline (MouseCHD) can accurately and automatically detect congenital heart defects in murine µCT scans, accelerating preclinical mechanistic studies of heart morphogenesis.
Micro-computed tomography (μCT) provides 3D images of congenital heart defects (CHD) in mice. However, diagnosing CHD from μCT scans is time-consuming and requires clinical expertise. Here, we present a deep learning approach to automatically segment and screen normal from malformed hearts. On a cohort of 139 μCT scans of control and mutant mice, our diagnosis model achieves an area-under-the-curve (AUC) of 97%. For further validation, we acquired two additional cohorts after model training. Performance on a similar 'prospective' cohort is excellent (AUC: 100%). Performance on a 'divergent' cohort containing novel genotypes is moderate (AUC: 81%), but improves markedly after model finetuning (AUC: 91%), showcasing robustness and adaptability to technical and biological differences in the data. A user-friendly Napari plugin allows researchers without coding expertise to utilize and retrain the model. Our pipeline will accelerate diagnosis of heart anomalies in mice and facilitate mechanistic studies of CHD.
Nguyen et al. (Tue,) studied this question.
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