Standard
Cascaded Training Pipeline for 3D Brain Tumor Segmentation. / Luu, Minh Sao Khue; Pavlovskiy, Evgeniy.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers. ed. / Alessandro Crimi; Spyridon Bakas. Springer Science and Business Media Deutschland GmbH, 2022. p. 410-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12962 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Luu, MSK
& Pavlovskiy, E 2022,
Cascaded Training Pipeline for 3D Brain Tumor Segmentation. in A Crimi & S Bakas (eds),
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12962 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 410-420, 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online,
27.09.2021.
https://doi.org/10.1007/978-3-031-08999-2_35APA
Luu, M. S. K.
, & Pavlovskiy, E. (2022).
Cascaded Training Pipeline for 3D Brain Tumor Segmentation. In A. Crimi, & S. Bakas (Eds.),
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers (pp. 410-420). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12962 LNCS). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-031-08999-2_35Vancouver
Luu MSK
, Pavlovskiy E.
Cascaded Training Pipeline for 3D Brain Tumor Segmentation. In Crimi A, Bakas S, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 410-420. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-08999-2_35
Author
Luu, Minh Sao Khue
; Pavlovskiy, Evgeniy. /
Cascaded Training Pipeline for 3D Brain Tumor Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers. editor / Alessandro Crimi ; Spyridon Bakas. Springer Science and Business Media Deutschland GmbH, 2022. pp. 410-420 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{e7bdaf7b80e04113bf6bb7e679c71258,
title = "Cascaded Training Pipeline for 3D Brain Tumor Segmentation",
abstract = "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.",
keywords = "3D U-Net, Brain tumor segmentation, Medical image segmentation",
author = "Luu, {Minh Sao Khue} and Evgeniy Pavlovskiy",
note = "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: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2022",
doi = "10.1007/978-3-031-08999-2_35",
language = "English",
isbn = "9783031089985",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "410--420",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers",
address = "Germany",
}
RIS
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 -
