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

机译:朝向高通量小鼠胚胎表型分类:一种对心室间隔缺陷进行分类的新方法

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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:国际小鼠表型联盟的目标(IMPC,www.mousephenotype.org)是通过逐一敲除小鼠中的所有超过23,000个基因,以进行比较分析。已经提高了大量的敲除鼠标线,导致对高吞吐量表型技术的强烈需求。传统意味着通过耗时的组织学检查在这种情况下显然不适合。因此,诸如CT和MRI的生物医学成像技术已经开始用于开发更有效的表型方法。然而,现有的工作主要是在解剖结构上依赖于体积分析来检测异常,然而,当特征是心脏细微的微妙时,这种类型的方法通常失败,并且同时表型评估通常需要专家体力劳动。本研究提出了,据我们所知,鼠标胚胎的第一个自动VSD诊断系统。我们的算法开始使用野生型鼠标图像创建图表,然后将敲除登记到地图集,以在心脏上进行基于地图集的分段,然后使用该区域的脑室分段进一步改进。通过检查左侧和右心室之间的重叠存在来完成VSD分类。我们的方法已在14个小鼠胚胎图像的数据库上验证,并达到了90.9%的整体准确性,灵敏度为66.7%,特异性为100%。

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