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Towards high-throughput mouse embryonic phenotyping: a novel approach to classifying ventricular septal defects

机译:迈向高通量小鼠胚胎表型分析:一种新的分类室间隔缺损的方法

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The goal of the International Mouse Phenotyping Consortium (IMPC, www.mousephenotype.org) is to study all the over 23,000 genes in the mouse by knocking them out one-by-one for comparative analysis. Large amounts of knockout mouse lines have been raised, leading to a strong demand for high-throughput phenotyping technologies. Traditional means via time-consuming histological examination is clearly unsuitable in this scenario. Biomedical imaging technologies such as CT and MRI therefore have started being used to develop more efficient phenotyping approaches. Existing work however primarily rests on volumetric analytics over anatomical structures to detect anomaly, yet this type of methods generally fail when features are subtle such as ventricular septal defects (VSD) in the heart, and meanwhile phenotypic assessment normally requires expert manual labor. This study proposes, to the best of our knowledge, the first automatic VSD diagnostic system for mouse embryos. Our algorithm starts with the creation of an atlas using wild-type mouse images, followed by registration of knockouts to the atlas to perform atlas-based segmentation on the heart and then ventricles, after which ventricle segmentation is further refined using a region growing technique. VSD classification is completed by checking the existence of an overlap between left and right ventricles. Our approach has been validated on a database of 14 mouse embryo images, and achieved an overall accuracy of 90.9%, with sensitivity of 66.7% and specificity of 100%.
机译:国际小鼠表型研究协会(IMPC,www.mousephenotype.org)的目标是通过逐一敲除小鼠中超过23,000个基因进行比较分析来研究它们。已经提出了大量的敲除小鼠系,导致对高通量表型技术的强烈需求。在这种情况下,通过费时的组织学检查的传统方法显然不适合。因此,诸如CT和MRI之类的生物医学成像技术已开始用于开发更有效的表型分析方法。但是,现有的工作主要取决于对解剖结构的体积分析以检测异常,但是,当诸如心脏的室间隔缺损(VSD)等细微的特征时,这种类型的方法通常会失败,同时,表型评估通常需要专业的体力劳动。据我们所知,本研究提出了第一个用于小鼠胚胎的自动VSD诊断系统。我们的算法始于使用野生型小鼠图像创建图谱,然后将敲除基因与图谱配准,以在心脏上进行基于图谱的分割,然后在心室上进行分割,然后使用区域生长技术进一步完善心室分割。通过检查左心室和右心室之间是否存在重叠来完成VSD分类。我们的方法已经在14个小鼠胚胎图像的数据库上得到验证,并获得了90.9%的总体准确度,66.7%的灵敏度和100%的特异性。

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