Dishane Luximon

Graduate Student Researcher
Department of Radiation Oncology
University of California, Los Angeles

Dishane earned his bachelorís degree in physics from Connecticut College while doing research in active galactic nuclei with the astrophysics department. He joined the Physics & Biology in Medicine Graduate Program in 2019 at UCLA, where he is currently pursuing his PhD in medical physics. Since joining the RO-SAML group in 2020, Dishane has been focussing on the development and implementation of AI-based tools, including organ segmentation and error detection tools, to enhance the radiation therapy treatment planning process and patient safety. His PhD dissertation topic involves the development of a deep learning-based patient setup error detection system to consolidate the safety of CBCT-guided radiotherapy. Dishane is also actively involved in global health initiatives as a student member of the Global Needs and Assessment Committee of the AAPM. Born and raised in Mauritius, Dishane is particularly interested in advancing global health aims to ensure more equitable access to high-quality cancer care on a global scale by utilizing his medical physics and AI expertise. Outside of work, Dishane enjoys exploring new cultures, cooking, and playing soccer.


Recent Publications:

  1. Luximon, D. C., Neylon, J., & Lamb, J. M. (2023). Feasibility of a deep-learning based anatomical region labeling tool for Cone-Beam Computed Tomography scans in radiotherapy. Physics and Imaging in Radiation Oncology, 25, 100427.
  2. Neylon, J., Luximon, D. C., Ritter, T., & Lamb, J. M. (2023). Proof-of-concept study of artificial intelligence-assisted review of CBCT image guidance. Journal of Applied Clinical Medical Physics, e14016.
  3. Luximon, D. C., Ritter, T., Fields, E., Neylon, J., Petragallo, R., Abdulkadir, Y., Charters J., Low D.A., & Lamb, J. M. (2022). Development and interinstitutional validation of an automatic vertebral-body misalignment error detector for cone-beam CT-guided radiotherapy. Medical Physics, 49(10), 6410-6423.
  4. Luximon, D. C., Abdulkadir, Y., Chow, P. E., Morris, E. D., & Lamb, J. M. (2022). Machine-assisted interpolation algorithm for semi-automated segmentation of highly deformable organs. Medical physics, 49(1), 41-51.

Open-Source Code:

Anatomical Region Labeling Tool for CBCT Scans: