Narrowing down specific regions in craniofacial CT data in real clinical scenes

Abstract

In machine learning, including machine learning, it is desirable to deal with a database that is organized under certain conditions. Currently, we are carrying out a project to collect craniofacial CT images used to diagnose facial bone fractures. The data from each facility is obtained with different CT equipment, exposing and imaging conditions are not standardized. The slice width is diverse. In our previous experience, accuracy improved by narrowing down the input region, when estimating feature point coordinates in public databases of craniofacial images. In this study, the previously mentioned data from real clinical scenes were processed to meet certain conditions, and cropped specific regions, using deep learning methods in combination. Results: The actual clinical data was able to be formatted in a certain way and narrowed down region almost automatically.