The increased rate of Total Hip Replacement (THR) for relieving hip pain and improving the quality of life has been accompanied by a rise in associated post-operative complications, which are evaluated and monitored mainly through clinical assessment of the X-ray images. The current clinical practice depends on the manual identification of important regions and the analysis of different features in arthroplasty X-ray images which can lead to subjectivity, prone to human error and delay diagnosis. Deep Learning (DL) based techniques showed outstanding outcomes across various image analysis tasks. However, the success of these networks is subjected to the availability of a very large, accurately annotated and well-balanced dataset - a constraint that is considered a main challenge for many medical image analysis tasks including THR.
This thesis focuses on automating the analysis of THR X-ray images to aid in the diagnosis and treatment planning of various THR complications. THR X-ray images including post-operation images and after Peri-Prosthetic Femur Fracture (PFF) images of a wide range of implants and various positioning and orientations, are collected to this end. Different Convolutional Neural Network (CNN) architectures are explored for PFF classification to observe how these networks perform in the presence of class imbalance and a limited number of data and with complex image patterns, either using full X-ray images or Region of Interest (ROI) images. This demonstrates that typical CNN-based methods succeeded in detecting PFF with DenseNet achieving an F1 score of 95%, while exhibiting low performance in the classification of PFF types, achieving an F1 score of 54% with GoogleNet, Resnet and DenseNet. This lower performance is attributed to the increased complexity of the task and the imbalanced distribution of the classes. To this end, the incorporation of THR medical knowledge with DL model is investigated.
The segmentation of the femoral implant component and the detection of important landmarks are formulated as simultaneous tasks within multi-task CNN that combines segmentation maps of implant with the regression of shape parameters derived from the Statistical Shape Model (SSM).
Compared to the state-of-the-art, this integrated approach improves the estimation of the implant shape by a 6% dice score, making the segmentation realistic and allowing automatic detection of the important landmarks which can help in detecting many THR complications.
For PFF diagnosis, the incorporation of the clinical process of interpreting THR X-ray images with CNN is developed. For this purpose, the process of clinical interpretation of PFF X-ray images is defined and the method is designed accordingly. Four feature extraction components are trained to construct features from distinctive regions of the X-ray image that are defined automatically. The extracted features are fused to classify the X-ray image into a specific fracture type. The developed approach improved PFF diagnosis by approximately 8% AUC score compared to state-of-the-art methods, signifying notable clinical advancement.
Finally, the virtual pre-operative planning of bone fracture reduction surgery is explored which is important to reduce surgery time and minimize potential risks. The main obstacle toward the planning task is to define the matching between fragments. Therefore, 3D puzzle-solving method is formulated by introducing a new fragment representation and feature extraction method that improves the matching between fragments. The initial evaluation of the method demonstrates promising performance for the virtual reassembly of broken objects.
