Xie Zhongliu, Liang Xi, Guo Liucheng, Kitamoto Asanobu, Tamura Masaru, Shiroishi Toshihiko, Gillies Duncan
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.
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.
J Med Imaging (Bellingham). 2015 Oct;2(4):041003. doi: 10.1117/1.JMI.2.4.041003. Epub 2015 Sep 11.
Intensive international efforts are underway toward phenotyping the entire mouse genome by modifying all its [Formula: see text] 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.
国际上正在进行密集的努力，通过逐一修改小鼠的所有[公式：见文本]基因来对整个小鼠基因组进行表型分析，以进行比较研究。如此规模的工作量引发了众多利用图像信息学来识别形态缺陷的研究。然而，这方面现有的工作主要基于通过野生型和基因修饰小鼠之间的结构体积测量来检测异常，当病理情况不涉及严重的体积变化时，比如心脏室间隔缺损（VSDs），这种方法通常就会失效。此外，在胚胎心脏表型分析中，胚胎心脏分割方面缺乏相关工作、公共图谱的可用性有限，以及在检测到异常后实际表型分类通常需要人工操作等其他限制因素，共同制约了现有做法满足高通量需求。据我们所知，本研究提出了小鼠胚胎成像中首个全自动VSD分类框架。我们的方法利用基于图谱的分割和蛇形演化技术相结合来得出心室的分割结果，其中通过检查左心室和右心室是否相邻或相互重叠来实现VSD分类。一项初步研究在概念验证层面验证了我们的方法，并通过在一个包含15张图像的数据库上进行的一系列实证实验，实现了100%的分类准确率。
