Quantum Computing Based Image Segmentation for Treatment Planning Applications

Advisor: David T. Fuentes, PhD

The rapid growth of quantum computing has resulted in its use in medical radiology. Physicians and biomedical researchers are looking at ways in which quantum computing can help improve healthcare. Several world-wide efforts are now incorporating quantum computing in medical radiology and health care applications. With such a demand for quantum computing to help improve clinical care and in medical research, we investigated the readiness of quantum computing for medical physicists’ applications. An accessible introduction to quantum computing will be presented to familiarize medical physicists with quantum computing. We apply currently available quantum computing based auto-contouring methods to image segmentation to evaluate them for clinical treatment planning. These implementations serve as examples of existing quantum algorithms, which include algorithms for quantum annealing hardware and gate-based quantum computers. The algorithms are applied to a magnetic resonance generated abdominal images for auto-contouring the liver. The results demonstrate that quantum auto-contouring methods are still nascent, unless coupled with classical artificial intelligence-based auto-contouring methods.

Advisory Committee:
David T. Fuentes, PhD, Chair
James A. Bankson, PhD
Jason R. Stafford, PhD
Tucker J. Netherton, PhD
Richard E. Wendt III, PhD

Attend via Zoom
Password: 226589