TY - GEN
T1 - An UNet-Based Brain Tumor Segmentation Framework via Optimal Mass Transportation Pre-processing
AU - Liao, Jia Wei
AU - Huang, Tsung Ming
AU - Li, Tiexiang
AU - Lin, Wen Wei
AU - Wang, Han
AU - Yau, Shing Tung
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This article aims to build a framework for MRI images of brain tumor segmentation using the deep learning method. For this purpose, we develop a novel 2-Phase UNet-based OMT framework to increase the ratio of brain tumors using optimal mass transportation (OMT). Moreover, due to the scarcity of training data, we change the density function by different parameters to increase the data diversity. For the post-processing, we propose an adaptive ensemble procedure by solving the eigenvectors of the Dice similarity matrix and choosing the result with the highest aggregation probability as the predicted label. The Dice scores of the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions for online validation computed by SegResUNet were 0.9214, 0.8823, and 0.8411, respectively. Compared with random crop pre-processing, OMT is far superior.
AB - This article aims to build a framework for MRI images of brain tumor segmentation using the deep learning method. For this purpose, we develop a novel 2-Phase UNet-based OMT framework to increase the ratio of brain tumors using optimal mass transportation (OMT). Moreover, due to the scarcity of training data, we change the density function by different parameters to increase the data diversity. For the post-processing, we propose an adaptive ensemble procedure by solving the eigenvectors of the Dice similarity matrix and choosing the result with the highest aggregation probability as the predicted label. The Dice scores of the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions for online validation computed by SegResUNet were 0.9214, 0.8823, and 0.8411, respectively. Compared with random crop pre-processing, OMT is far superior.
KW - 2-Phase OMT framework
KW - Brain Tumor Segmentation
KW - Density function
KW - Optimal Mass Transportation
KW - UNet
UR - http://www.scopus.com/inward/record.url?scp=85172287341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172287341&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-33842-7_19
DO - 10.1007/978-3-031-33842-7_19
M3 - Conference contribution
AN - SCOPUS:85172287341
SN - 9783031338410
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 216
EP - 228
BT - Brainlesion
A2 - Bakas, Spyridon
A2 - Baid, Ujjwal
A2 - Baheti, Bhakti
A2 - Crimi, Alessandro
A2 - Malec, Sylwia
A2 - Pytlarz, Monika
A2 - Zenk, Maximilian
A2 - Dorent, Reuben
PB - Springer Science and Business Media Deutschland GmbH
T2 - Proceedings of the 8th International MICCAI Brainlesion Workshop, BrainLes 2022
Y2 - 18 September 2022 through 22 September 2022
ER -