Date of Award


Document Type

Masters Thesis

Degree Name


Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science, Mechanical and Materials Engineering

First Advisor

Paul J. Rullkoetter, Ph.D.


Computer vision, Convolutional neural networks, Deep learning, Machine learning, Orthopaedics


The reemergence of deep learning in recent years has led to its successful application in a wide variety of fields. As a subfield of machine learning, deep learning offers an array of powerful algorithms for data-driven applications. Orthopaedics stands to benefit from the potential of deep learning for advancements in the field. This thesis investigated applications of deep learning for the field of orthopaedics through the development of three distinct projects.

First, algorithms were developed for the automatic segmentation of the structures in the knee from MRI. The resulting algorithms can be used to accurately segment full MRI scans in a matter of seconds. Reconstructed structures from predicted segmentation maps yielded on average submillimeter geometric errors when compared to geometries from ground truth segmentation maps on a test set. The resulting frameworks can further be applied to develop algorithms for automatic segmentation of other anatomies and modalities in the future.

Next, neural networks (NNs) were developed and evaluated for the prediction of muscle and joint reaction forces of patients performing activities of daily living (ADLs) in a gait lab environment. The performance of these models demonstrates the potential of NNs to supplement traditional gait lab data collection and has implications for the development of new gait lab workflows with less hardware and time requirements. Additionally, the models performed activity classification using standard gait lab data with near-perfect accuracy.

Lastly, a deep learning-based computer vision system was developed for the detection and 6-degree of freedom (6-DoF) pose estimation of two surgical tracking tools routinely used in total knee replacement (TKR). The resulting model demonstrated competitive object detection capabilities and translation error as little as a few centimeters for the pose estimation task. A preliminary evaluation of the system shows promise for its applications in skill assessment and operations research.

The development of these three projects represents a significant step towards the adoption of deep learning methodologies by the field of orthopaedics and shows potential for future additional applications.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

William Stewart Burton


Received from ProQuest

File Format




File Size

131 p.


Biomedical engineering, Medical imaging, Biomechanics