TY - GEN
T1 - Brain tumor segmentation on multimodal MR imaging using multi-level upsampling in decoder
AU - Hu, Yan
AU - Liu, Xiang
AU - Wen, Xin
AU - Niu, Chen
AU - Xia, Yong
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Accurate brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose the multi-level up-sampling network (MU-Net) to learn the image presentations of transverse, sagittal and coronal view and fuse them to automatically segment brain tumors, including necrosis, edema, non-enhancing, and enhancing tumor, in multimodal magnetic resonance (MR) sequences. The MU-Net model has an encoder–decoder structure, in which low level feature maps obtained by the encoder and high level feature maps obtained by the decoder are combined by using a newly designed global attention (GA) module. The proposed model has been evaluated on the BraTS 2018 Challenge validation dataset and achieved an average Dice similarity coefficient of 0.88, 0.74, 0.69 and 0.85, 0.72, 0.66 for the whole tumor, core tumor and enhancing tumor on the validation dataset and testing dataset, respectively. Our results indicate that the proposed model has a promising performance in automated brain tumor segmentation.
AB - Accurate brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose the multi-level up-sampling network (MU-Net) to learn the image presentations of transverse, sagittal and coronal view and fuse them to automatically segment brain tumors, including necrosis, edema, non-enhancing, and enhancing tumor, in multimodal magnetic resonance (MR) sequences. The MU-Net model has an encoder–decoder structure, in which low level feature maps obtained by the encoder and high level feature maps obtained by the decoder are combined by using a newly designed global attention (GA) module. The proposed model has been evaluated on the BraTS 2018 Challenge validation dataset and achieved an average Dice similarity coefficient of 0.88, 0.74, 0.69 and 0.85, 0.72, 0.66 for the whole tumor, core tumor and enhancing tumor on the validation dataset and testing dataset, respectively. Our results indicate that the proposed model has a promising performance in automated brain tumor segmentation.
KW - Brain tumor segmentation
KW - Encoder–decoder
KW - Global attention
KW - Magnetic resonance imaging
KW - Multi-level upsampling
UR - http://www.scopus.com/inward/record.url?scp=85063435381&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-11726-9_15
DO - 10.1007/978-3-030-11726-9_15
M3 - 会议稿件
AN - SCOPUS:85063435381
SN - 9783030117252
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 168
EP - 177
BT - Brainlesion
A2 - Keyvan, Farahani
A2 - Crimi, Alessandro
A2 - van Walsum, Theo
A2 - Reyes, Mauricio
A2 - Bakas, Spyridon
A2 - Kuijf, Hugo
PB - Springer Verlag
T2 - 4th International MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -