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Proceedings Paper

High-throughput mouse phenotyping using non-rigid registration and robust principal component analysis
Author(s): Zhongliu Xie; Asanobu Kitamoto; Masaru Tamura; Toshihiko Shiroishi; Duncan Gillies
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Paper Abstract

Intensive international efforts are underway towards phenotyping the mouse genome, by knocking out each of its ≈25,000 genes one-by-one for comparative study. With vast amounts of data to analyze, the traditional method using time-consuming histological examination is clearly impractical, leading to an overwhelming demand for some high-throughput phenotyping framework, especially with the employment of biomedical image informatics to efficiently identify phenotypes concerning morphological abnormality. Existing work has either excessively relied on volumetric analytics which is insensitive to phenotypes associated with no severe volume variations, or tailored for specific defects and thus fails to serve a general phenotyping purpose. Furthermore, the prevailing requirement of an atlas for image segmentation in contrast to its limited availability further complicates the issue in practice. In this paper we propose a high-throughput general-purpose phenotyping framework that is able to efficiently perform batch-wise anomaly detection without prior knowledge of the phenotype and the need for atlas-based segmentation. Anomaly detection is centered on the combined use of group-wise non-rigid image registration and robust principal component analysis (RPCA) for feature extraction and decomposition.

Paper Details

Date Published: 21 March 2016
PDF: 8 pages
Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978415 (21 March 2016); doi: 10.1117/12.2217155
Show Author Affiliations
Zhongliu Xie, Imperial College London (United Kingdom)
National Institute of Informatics (Japan)
Asanobu Kitamoto, National Institute of Informatics (Japan)
Masaru Tamura, RIKEN Bioresource Ctr. (Japan)
Toshihiko Shiroishi, National Institute of Genetics (Japan)
Duncan Gillies, Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 9784:
Medical Imaging 2016: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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