Early Detection of Knee Osteoarthritis using Deep Learning-based MRI Features

2022-08-23

Background: Advancements in the field of artificial intelligence have lead to the incorporation of automated algorithms in the analysis of medical images and data. Deep learning algorithms have been applied in muscu- loskeletal research to improve the understanding of osteoarthritis and to assist in disease detection and prognosis. The majority of the developed methods examine and process X-ray images and clinical data (age, gender etc.), with a small minority using MRI as inputs. Objective: The current master thesis project aims to investigate the influence of MRI scans on the early detection of knee osteoarthritis through the use of deep learning architectures, and to develop a semi-automatic method for knee region of interest extraction for creating the MRI input of detection algorithms. Methods: The MRI scans used in this project were acquired from the publicly available database of the Os- teoarthritis Initiative. In total 593 dual echo steady state and intermediate-weighted turbo spin-echo sequences were included. The extraction of the knee joint included several processing steps. Initially, a U-Net model was trained on 507 annotated dual echo steady state MRIs for the segmentation of bone and cartilage tissue, which was followed by the registration of the output masks to intermediate-weighted turbo spin-echo sequences in order to create the joint labels for the desired MRI scans. Final step for the region of interest construction included the search of bone coordinates and the creation of the knee joint region of interest. The detection of early osteoarthri- tis progression from knee MRI scans was tested through three different deep learning architectures, a residual network (ResNet), a densely connected convolutional network (DenseNet) and a convolutional variational au- toencoder (CVAE). Furthermore, the probability output of the ResNet and DenseNet as well as the feature vector of the CVAE were coupled with clinical data (age, gender, bone mass index) and
Biomedical Engineering

Open access content. Open access content
© 2022 Anastasis Alexopoulos

English

NLTUD oai:tudelft.nl:uuid:00bb9de9-73df-4733-894e-6e12fa98fef1
1344325983

DELFT UNIV OF TECHNOL
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