Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping

Zhongliu Xie; Xi Liang; Liucheng Guo; Asanobu Kitamoto; Masaru Tamura; Toshihiko Shiroishi; Duncan F. Gillies

doi:10.1117/1.JMI.2.4.041003

11 September 2015 Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping

Zhongliu Xie, Xi Liang, Liucheng Guo, Asanobu Kitamoto, Masaru Tamura, Toshihiko Shiroishi, Duncan F. Gillies

Author Affiliations +

Journal of Medical Imaging, Vol. 2, Issue 4, 041003 (September 2015). https://doi.org/10.1117/1.JMI.2.4.041003

Abstract

Intensive international efforts are underway toward phenotyping the entire mouse genome by modifying all its ≈25,000 genes one-by-one for comparative studies. A workload of this scale has triggered numerous studies harnessing image informatics for the identification of morphological defects. However, existing work in this line primarily rests on abnormality detection via structural volumetrics between wild-type and gene-modified mice, which generally fails when the pathology involves no severe volume changes, such as ventricular septal defects (VSDs) in the heart. Furthermore, in embryo cardiac phenotyping, the lack of relevant work in embryonic heart segmentation, the limited availability of public atlases, and the general requirement of manual labor for the actual phenotype classification after abnormality detection, along with other limitations, have collectively restricted existing practices from meeting the high-throughput demands. This study proposes, to the best of our knowledge, the first fully automatic VSD classification framework in mouse embryo imaging. Our approach leverages a combination of atlas-based segmentation and snake evolution techniques to derive the segmentation of heart ventricles, where VSD classification is achieved by checking whether the left and right ventricles border or overlap with each other. A pilot study has validated our approach at a proof-of-concept level and achieved a classification accuracy of 100% through a series of empirical experiments on a database of 15 images.

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Zhongliu Xie, Xi Liang, Liucheng Guo, Asanobu Kitamoto, Masaru Tamura, Toshihiko Shiroishi, and Duncan F. Gillies "Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping," Journal of Medical Imaging 2(4), 041003 (11 September 2015). https://doi.org/10.1117/1.JMI.2.4.041003

Published: 11 September 2015

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