TY - GEN
T1 - Joint Learning of Segmentation and Overall Survival for Brain Tumor based on U-Net
AU - Kwon, Junmo
AU - Park, Hyunjin
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The prognosis, hence survival, of patients with brain tumors is highly dependent on the size and grade of the tumor. Thus, joint learning of brain tumor segmentation and overall survival of patients with brain tumors can benefit each other. In this work, we explored the feasibility of prediction of patients' overall survival through U-Net guided by the information of brain tumor segmentation. We evaluated the proposed model on the multimodal brain tumor segmentation (BraTS) 2017 challenge dataset. We achieved the mean Dice score of 0.595 for brain tumor segmentation on the test set. The Pearson correlation coefficient for overall survival prediction on the test set was 0.243, indicating promising results for both brain tumor segmentation and overall survival prediction.
AB - The prognosis, hence survival, of patients with brain tumors is highly dependent on the size and grade of the tumor. Thus, joint learning of brain tumor segmentation and overall survival of patients with brain tumors can benefit each other. In this work, we explored the feasibility of prediction of patients' overall survival through U-Net guided by the information of brain tumor segmentation. We evaluated the proposed model on the multimodal brain tumor segmentation (BraTS) 2017 challenge dataset. We achieved the mean Dice score of 0.595 for brain tumor segmentation on the test set. The Pearson correlation coefficient for overall survival prediction on the test set was 0.243, indicating promising results for both brain tumor segmentation and overall survival prediction.
KW - convolutional neural network
KW - deep learning
KW - magnetic resonance imaging
KW - overall survival prediction
KW - semantic segmentation
UR - https://www.scopus.com/pages/publications/85166476528
U2 - 10.1109/CBMS58004.2023.00345
DO - 10.1109/CBMS58004.2023.00345
M3 - Conference contribution
AN - SCOPUS:85166476528
T3 - Proceedings - IEEE Symposium on Computer-Based Medical Systems
SP - 925
EP - 926
BT - Proceedings - 2023 IEEE 36th International Symposium on Computer-Based Medical Systems, CBMS 2023
A2 - Sicilia, Rosa
A2 - Kane, Bridget
A2 - Almeida, Joao Rafael
A2 - Spiliopoulou, Myra
A2 - Andrades, Jose Alberto Benitez
A2 - Placidi, Giuseppe
A2 - Gonzalez, Alejandro Rodriguez
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023
Y2 - 22 June 2023 through 24 June 2023
ER -