TY - GEN
T1 - A Two-Phase Optimal Mass Transportation Technique for 3D Brain Tumor Detection and Segmentation
AU - Lin, Wen Wei
AU - Li, Tiexiang
AU - Huang, Tsung Ming
AU - Lin, Jia Wei
AU - Yueh, Mei Heng
AU - Yau, Shing Tung
N1 - Funding Information:
This work was partially supported by the Ministry of Science and Technology (MoST), the National Center for Theoretical Sciences, the Big Data Computing Center of Southeast University, the Nanjing Center for Applied Mathematics, the ST Yau Center in Taiwan, and the Shing-Tung Yau Center at Southeast University. W.-W. Lin, T.-M. Huang, and M.-H. Yueh were partially supported by MoST 110-2115-M-A49-004-, 110-2115-M-003-012-MY3, and 109-2115-M-003-010-MY2 and 110-2115-M-003-014-, respectively. T. Li was supported in part by the National Natural Science Foundation of China (NSFC) 11971105.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The goal of optimal mass transportation (OMT) is to transform any irregular 3D object (i.e., a brain image) into a cube without creating significant distortion, which is utilized to preprocess irregular brain samples to facilitate the tensor form of the input format of the U-net algorithm. The BraTS 2021 database newly provides a challenging platform for the detection and segmentation of brain tumors, namely, the whole tumor (WT), the tumor core (TC) and the enhanced tumor (ET), by AI techniques. We propose a two-phase OMT algorithm with density estimates for 3D brain tumor segmentation. In the first phase, we construct a volume-mass-preserving OMT via the density determined by the FLAIR grayscale of the scanned modality for the U-net and predict the possible tumor regions. Then, in the second phase, we increase the density on the region of interest and construct a new OMT to enlarge the target region of tumors for the U-net so that the U-net has a better chance to learn how to mark the correct segmentation labels. The application of this preprocessing OMT technique is a new and trending method for CNN training and validation.
AB - The goal of optimal mass transportation (OMT) is to transform any irregular 3D object (i.e., a brain image) into a cube without creating significant distortion, which is utilized to preprocess irregular brain samples to facilitate the tensor form of the input format of the U-net algorithm. The BraTS 2021 database newly provides a challenging platform for the detection and segmentation of brain tumors, namely, the whole tumor (WT), the tumor core (TC) and the enhanced tumor (ET), by AI techniques. We propose a two-phase OMT algorithm with density estimates for 3D brain tumor segmentation. In the first phase, we construct a volume-mass-preserving OMT via the density determined by the FLAIR grayscale of the scanned modality for the U-net and predict the possible tumor regions. Then, in the second phase, we increase the density on the region of interest and construct a new OMT to enlarge the target region of tumors for the U-net so that the U-net has a better chance to learn how to mark the correct segmentation labels. The application of this preprocessing OMT technique is a new and trending method for CNN training and validation.
KW - Irregular 3D image
KW - Optimal mass transportation
KW - Two-phase OMT
KW - Volume-measure-preserving map
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U2 - 10.1007/978-3-031-08999-2_34
DO - 10.1007/978-3-031-08999-2_34
M3 - Conference contribution
AN - SCOPUS:85135029789
SN - 9783031089985
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 400
EP - 409
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
Y2 - 27 September 2021 through 27 September 2021
ER -