MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

Benjamin B. Yan; Yujia Wei; Jaidip Manikrao M. Jagtap; Mana Moassefi; Diana V.Vera Garcia; Yashbir Singh; Sanaz Vahdati; Shahriar Faghani; Bradley J. Erickson; Gian Marco Conte

doi:10.1007/978-3-031-09002-8_7

MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

Benjamin B. Yan, Yujia Wei, Jaidip Manikrao M. Jagtap, Mana Moassefi, Diana V.Vera Garcia, Yashbir Singh, Sanaz Vahdati, Shahriar Faghani, Bradley J. Erickson, Gian Marco Conte

Radiology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation time without compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of the brain volume, and combines the model predictions at the 2D slice level into a full 3D segmentation that is written into a NIfTI file. On the official 2021 BraTS test set of 570 cases, the model obtained median Dice scores of 0.80, 0.87, and 0.87, as well as median 95% Hausdorff distances of 2.45, 4.64, and 6.40 for the enhancing tumor, tumor core, and whole tumor regions, respectively.

Original language	English (US)
Title of host publication	Brainlesion
Subtitle of host publication	Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Revised Selected Papers
Editors	Alessandro Crimi, Spyridon Bakas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	80-89
Number of pages	10
ISBN (Print)	9783031090011
DOIs	https://doi.org/10.1007/978-3-031-09002-8_7
State	Published - 2022
Event	7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: Sep 27 2021 → Sep 27 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12963 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	9/27/21 → 9/27/21

Keywords

Glioblastoma
MRI
Segmentation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-031-09002-8_7

Cite this

Yan, B. B., Wei, Y., Jagtap, J. M. M., Moassefi, M., Garcia, D. V. V., Singh, Y., Vahdati, S., Faghani, S., Erickson, B. J., & Conte, G. M. (2022). MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. 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. 80-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12963 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-09002-8_7

MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. / Yan, Benjamin B.; Wei, Yujia; Jagtap, Jaidip Manikrao M. et al.
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. 80-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12963 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yan, BB, Wei, Y, Jagtap, JMM, Moassefi, M, Garcia, DVV, Singh, Y, Vahdati, S, Faghani, S, Erickson, BJ & Conte, GM 2022, MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. 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. 12963 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 80-89, 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 9/27/21. https://doi.org/10.1007/978-3-031-09002-8_7

Yan BB, Wei Y, Jagtap JMM, Moassefi M, Garcia DVV, Singh Y et al. MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. 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. 80-89. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-09002-8_7

Yan, Benjamin B. ; Wei, Yujia ; Jagtap, Jaidip Manikrao M. et al. / MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. 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. 80-89 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks",

abstract = "In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation time without compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of the brain volume, and combines the model predictions at the 2D slice level into a full 3D segmentation that is written into a NIfTI file. On the official 2021 BraTS test set of 570 cases, the model obtained median Dice scores of 0.80, 0.87, and 0.87, as well as median 95% Hausdorff distances of 2.45, 4.64, and 6.40 for the enhancing tumor, tumor core, and whole tumor regions, respectively.",

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AU - Singh, Yashbir

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N2 - In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation time without compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of the brain volume, and combines the model predictions at the 2D slice level into a full 3D segmentation that is written into a NIfTI file. On the official 2021 BraTS test set of 570 cases, the model obtained median Dice scores of 0.80, 0.87, and 0.87, as well as median 95% Hausdorff distances of 2.45, 4.64, and 6.40 for the enhancing tumor, tumor core, and whole tumor regions, respectively.

AB - In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation time without compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of the brain volume, and combines the model predictions at the 2D slice level into a full 3D segmentation that is written into a NIfTI file. On the official 2021 BraTS test set of 570 cases, the model obtained median Dice scores of 0.80, 0.87, and 0.87, as well as median 95% Hausdorff distances of 2.45, 4.64, and 6.40 for the enhancing tumor, tumor core, and whole tumor regions, respectively.

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ER -

MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks

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Keywords

ASJC Scopus subject areas

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