Deep Learning for Automated Segmentation of the Hip Joint in X-ray Images
A study of the accuracy of a ResUNet-based approach for predicting the minimum joint space width along the weight-bearing part of the hip joint in a 2D image, in comparison to BoneFinder ground-truth data
D. Ileana (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Gijs van Tulder – Mentor
M.A. van den Berg – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
JH Krijthe – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Xucong Zhang – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Hip osteoarthritis is a widespread disease, with medical experts facing difficulties in this illness, due to a lack of standard grading score. Nevertheless, the minimum joint space width remains the most important score for osteoarthritis severity. Manual estimation of this metric is a tedious task, which can greatly benefit from employing an automated tool. While some research has been done in developing such a tool using deep learning methods, a novel and promising approach, most lack annotated data to use for training, which can be hard to obtain. Thus, thus research aims at developing a deep learning approach towards estimating the minimum joint space, while using automatically labels produced with an existing algorithm.