TY - GEN

T1 - Cascaded Training Pipeline for 3D Brain Tumor Segmentation

AU - Luu, Minh Sao Khue

AU - Pavlovskiy, Evgeniy

N1 - Funding Information: We would like to express our appreciation to the Stream Data Analytics and Machine Learning Laboratory for providing office places and computational resources to complete this research. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - We apply a cascaded training pipeline for the 3D U-Net to segment each brain tumor sub-region separately and chronologically. Firstly, the volumetric data of four modalities are used to segment the whole tumor in the first round of training. Then, our model combines the whole tumor segmentation with the mpMRI images to segment the tumor core. Finally, the network uses whole tumor and tumor core segmentations to predict enhancing tumor regions. Unlike the standard 3D U-Net, we use Group Normalization and Randomized Leaky Rectified Linear Unit in the encoding and decoding blocks. We achieved dice scores on the validation set of 88.84, 81.97, and 75.02 for whole tumor, tumor core, and enhancing tumor, respectively.

AB - We apply a cascaded training pipeline for the 3D U-Net to segment each brain tumor sub-region separately and chronologically. Firstly, the volumetric data of four modalities are used to segment the whole tumor in the first round of training. Then, our model combines the whole tumor segmentation with the mpMRI images to segment the tumor core. Finally, the network uses whole tumor and tumor core segmentations to predict enhancing tumor regions. Unlike the standard 3D U-Net, we use Group Normalization and Randomized Leaky Rectified Linear Unit in the encoding and decoding blocks. We achieved dice scores on the validation set of 88.84, 81.97, and 75.02 for whole tumor, tumor core, and enhancing tumor, respectively.

KW - 3D U-Net

KW - Brain tumor segmentation

KW - Medical image segmentation

UR - http://www.scopus.com/inward/record.url?scp=85135040781&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/b4d824f8-f92e-3edd-b93b-25327c9bc80b/

U2 - 10.1007/978-3-031-08999-2_35

DO - 10.1007/978-3-031-08999-2_35

M3 - Conference contribution

AN - SCOPUS:85135040781

SN - 9783031089985

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 410

EP - 420

BT - Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers

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 -