Document Type
Conference Paper
Publication Date
2010
DOI
10.1117/12.844035
Publication Title
Medical Imaging 2010: Computer-Aided Diagnosis, Proceedings of SPIE Vol. 7624
Volume
7624
Pages
762425 (1-8)
Conference Name
Medical Imaging 2010: Computer-Aided Diagnosis, February 16-18, 2010, San Diego, California
Abstract
A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to tumor at a later time. In this preliminary study, we focused on the trend of brain tumor progression during three consecutive visits, i.e., visit A, B, and C. A machine learning algorithm was trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit A to visit C. Preliminary results showed that prediction for brain tumor progression is feasible. An average of 80.9% pixel-wise accuracy was achieved for tumor progression prediction at visit C.
Rights
Copyright 2010 Society of Photo‑Optical Instrumentation Engineers (SPIE).
One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.
Original Publication Citation
Shen, Y., Banerjee, D., Li, J., Chandler, A., Shen, Y., McKenzie, F., & Wang, J. (2010). Prediction of brain tumor progression using a machine learning technique. In N. Karssemeijer & R.M. Summers (Eds.), Medical Imaging 2010: Computer-Aided Diagnosis, Proceedings of SPIE Vol. 7624 (762425). SPIE of Bellingham, WA. https://doi.org/10.1117/12.844035
Repository Citation
Shen, Yuzhong; Banerjee, Debrup; Li, Jiang; Chandler, Adam; Shen, Yufei; McKenzie, Frederic D.; Wang, Jihong; Karssemeijer, Nico (Ed.); and Summers, Ronald M. (Ed.), "Prediction of Brain Tumor Progression Using a Machine Learning Technique" (2010). Electrical & Computer Engineering Faculty Publications. 392.
https://digitalcommons.odu.edu/ece_fac_pubs/392
ORCID
0000-0003-0091-6986 (Li), 0000-0002-0160-5605 (McKenzie)