ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1553539
This article is part of the Research TopicEmerging Fast Medical Imaging Techniques in RadiologyView all 3 articles
Evaluation of Quantum Contouring Algorithms for Treatment Planning on MR Abdominal Images
Provisionally accepted
- 1University of Texas MD Anderson Cancer Center, Houston, United States
- 2Department of Imaging Physics-Research, Division of Diagnostic Imaging, University of Texas MD Anderson Cancer Center, Houston, United States
- 3Department of Computational and Applied Mathematics and Operations Research, Rice University, Houston, United States
- 4Department of Imaging Physics-Research, Division of Diagnostic Imaging, Houston, United States
- 5Department of Gastrointestinal Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, United States
- 6Sheikh Admed Center for Pancreatic Cancer Research, University of Texas MD Anderson Cancer Center Houston, Houston, United States
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Quantum computing is increasingly being investigated for integration into medical radiology and healthcare applications worldwide. Given its potential to enhance clinical care and medical research, we developed an evaluation of quantum computing-based auto-contouring methods to introduce medical physicists to this emerging technology. We implemented existing quantum algorithms as prototypes tailored for specific quantum hardware, focusing on their application to auto-contouring in medical imaging. The evaluation was performed using a medical resonance imaging (MRI) abdominal dataset, comprising 102 patient scans. Our findings suggest that while quantum computing for auto-contouring shows promise, it remains in its early stages. At present, artificial intelligence-based algorithms continue to be the preferred choice for auto-contouring in treatment planning due to their greater efficiency and accuracy.
Keywords: Quantum computing, Medical image segmentation, Auto-contouring, Quantum image representation, Radiotherapy planning
Received: 30 Dec 2024; Accepted: 23 Jun 2025.
Copyright: © 2025 Glenn, Netherton, Celaya, Riviere, Koay and Fuentes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Rachel Glenn, University of Texas MD Anderson Cancer Center, Houston, United States
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