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<p>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.</p>

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Deep learning-based detection of murine congenital heart defects from µCT scans

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Hoa Nguyen,
  • Audrey Desgrange,
  • Amaia Ochandorena-Saa,
  • Vanessa Benhamo,
  • Ségolène Bernheim,
  • Lucile Houyel,
  • Sigolène M. Meilhac,
  • Christophe Zimmer

摘要

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.