TY - GEN
T1 - Segmentation then Prediction
T2 - 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
AU - Zhao, Guojing
AU - Jiang, Bowen
AU - Zhang, Jianpeng
AU - Xia, Yong
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Accurate brain tumor segmentation and survival prediction are two fundamental but challenging tasks in the computer aided diagnosis of gliomas. Traditionally, these two tasks were performed independently, without considering the correlation between them. We believe that both tasks should be performed under a unified framework so as to enable them mutually benefit each other. In this paper, we propose a multi-task deep learning model called segmentation then prediction (STP), to segment brain tumors and predict patient overall survival time. The STP model is composed of a segmentation module and a survival prediction module. The former uses 3D U-Net as its backbone, and the latter uses both local and global features. The local features are extracted by the last layer of the segmentation encoder, while the global features are produced by a global branch, which uses 3D ResNet-50 as its backbone. The STP model is jointly optimized for two tasks. We evaluated the proposed STP model on the BraTS 2020 validation dataset and achieved an average Dice similarity coefficient (DSC) of 0.790, 0.910, 0.851 for the segmentation of enhanced tumor core, whole tumor, and tumor core, respectively, and an accuracy of 65.5% for survival prediction.
AB - Accurate brain tumor segmentation and survival prediction are two fundamental but challenging tasks in the computer aided diagnosis of gliomas. Traditionally, these two tasks were performed independently, without considering the correlation between them. We believe that both tasks should be performed under a unified framework so as to enable them mutually benefit each other. In this paper, we propose a multi-task deep learning model called segmentation then prediction (STP), to segment brain tumors and predict patient overall survival time. The STP model is composed of a segmentation module and a survival prediction module. The former uses 3D U-Net as its backbone, and the latter uses both local and global features. The local features are extracted by the last layer of the segmentation encoder, while the global features are produced by a global branch, which uses 3D ResNet-50 as its backbone. The STP model is jointly optimized for two tasks. We evaluated the proposed STP model on the BraTS 2020 validation dataset and achieved an average Dice similarity coefficient (DSC) of 0.790, 0.910, 0.851 for the segmentation of enhanced tumor core, whole tumor, and tumor core, respectively, and an accuracy of 65.5% for survival prediction.
KW - Brain tumor segmentation
KW - Deep learning
KW - Joint learning
KW - MR image generation
KW - Survival prediction
UR - http://www.scopus.com/inward/record.url?scp=85107379374&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72084-1_44
DO - 10.1007/978-3-030-72084-1_44
M3 - 会议稿件
AN - SCOPUS:85107379374
SN - 9783030720834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 492
EP - 502
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 October 2020 through 4 October 2020
ER -