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Journal of Medical Imaging

Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping
Author(s): Zhongliu Xie; Xi Liang; Liucheng Guo; Asanobu Kitamoto; Masaru Tamura; Toshihiko Shiroishi; Duncan F. Gillies
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Paper 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.

Paper Details

Date Published: 11 September 2015
PDF: 9 pages
J. Med. Imag. 2(4) 041003 doi: 10.1117/1.JMI.2.4.041003
Published in: Journal of Medical Imaging Volume 2, Issue 4
Show Author Affiliations
Zhongliu Xie, Imperial College London (United Kingdom)
National Institute of Informatics (Japan)
Xi Liang, National Institute of Informatics (Japan)
The Univ. of Melbourne (Australia)
Liucheng Guo, Imperial College London (United Kingdom)
Asanobu Kitamoto, National Institute of Informatics (Japan)
Masaru Tamura, National Institute of Genetics (Japan)
RIKEN (Japan)
Toshihiko Shiroishi, National Institute of Genetics (Japan)
Duncan F. Gillies, Imperial College London (United Kingdom)

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