Special Section on Radiomics and Imaging Genomics

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

[+] Author Affiliations
Zhongliu Xie

Imperial College London, Department of Computing, South Kensington Campus, London SW7 2AZ, United Kingdom

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

Xi Liang

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

University of Melbourne, Department of Computer Science and Software Engineering, Parkville Campus, Melbourne VIC 3010, Australia

Liucheng Guo

Imperial College London, Department of Electrical and Electronic Engineering, South Kensington Campus, London SW7 2AZ, United Kingdom

Asanobu Kitamoto

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

Masaru Tamura

National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan

RIKEN BioResource Center, 3-1-1 Koyadai, Tsukuba, Ibaraki 305-0074, Japan

Toshihiko Shiroishi

National Institute of Genetics, 1111 Yata, Mishima, Shizuoka 411-8540, Japan

Duncan Gillies

Imperial College London, Department of Computing, South Kensington Campus, London SW7 2AZ, United Kingdom

J. Med. Imag. 2(4), 041003 (Sep 11, 2015). doi:10.1117/1.JMI.2.4.041003
History: Received April 1, 2015; Accepted July 30, 2015
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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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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Zhongliu Xie ; Xi Liang ; Liucheng Guo ; Asanobu Kitamoto ; Masaru Tamura, et al.
"Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping", J. Med. Imag. 2(4), 041003 (Sep 11, 2015). ; http://dx.doi.org/10.1117/1.JMI.2.4.041003


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