TY - GEN
T1 - Prediction of brain tumor progression using a machine learning technique
AU - Shen, Yuzhong
AU - Banerjee, Debrup
AU - Li, Jiang
AU - Chandler, Adam
AU - Shen, Yufei
AU - McKenzie, Frederic D.
AU - Wang, Jihong
N1 - Publisher Copyright:
© 2016 SPIE.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - DTI
KW - Machine Learning
KW - Tumor Progression Prediction
UR - http://www.scopus.com/inward/record.url?scp=84879551576&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879551576&partnerID=8YFLogxK
U2 - 10.1117/12.844035
DO - 10.1117/12.844035
M3 - Conference contribution
AN - SCOPUS:84879551576
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2010
A2 - Summers, Ronald M.
A2 - Karssemeijer, Nico
PB - SPIE
T2 - Medical Imaging 2010: Computer-Aided Diagnosis
Y2 - 16 February 2010 through 18 February 2010
ER -