2019 Volume 37 Issue 2 Pages 89-94
This article describes an image analysis project for extracting knowledge from a large scale heterogeneous (i.e., variations in facility, patient attributes, pathology and so on are very large) medical image database that several medical academic institutions supported by Japan Agency for Medical Research and Development (AMED) and National Institute of Informatics (NII) has been constructing. In this study, we specifically focus on the database organization and an example application in orthopedic surgery. The database organization uses an unsupervised learning framework by dimensionality reduction with t-SNE and k-means clustering. We selected the volumes that contains hip region from the heterogeneous large database and applied a deep-learning-based segmentation method to understand shape features of each anatomical part including bones and muscles. The statistical analysis of the shapes would allow us to understand musculoskeletal anatomy that helps diagnosis and planning in orthopedic surgery.
