Name
Title | ||
|---|---|---|
Towards high-throughput mouse embryonic phenotyping: a novel approach to classifying ventricular septal defects |
Abstract | ||
|---|---|---|
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%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2081148 | Proceedings of SPIE |
Keywords | Field | DocType |
mouse embryo phenotyping,ventricular septal defects,atlas-based segmentation,region growing | Medical imaging,Embryonic stem cell,Image segmentation,Artificial intelligence,Region growing,Ventricle,Computational biology,Knockout mouse,Computer vision,Segmentation,Anatomical structures,Bioinformatics,Physics | Conference |
Volume | ISSN | Citations |
9413 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Liang | 1 | 1 | 1.70 |
| zhongliu xie | 2 | 0 | 0.34 |
| Masaru Tamura | 3 | 1 | 1.39 |
| Toshihiko Shiroishi | 4 | 32 | 4.33 |
| Asanobu Kitamoto | 5 | 84 | 15.31 |