Lightweight U-Nets for Brain Tumor Segmentation

Tomasz Tarasiewicz; Michal Kawulok; Jakub Nalepa

doi:10.1007/978-3-030-72087-2_1

Lightweight U-Nets for Brain Tumor Segmentation

Department of Algorithmics and Software (RAU/RAU5)

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

28 Citations (Scopus)

Abstract

Automated brain tumor segmentation is a vital topic due to its clinical applications. We propose to exploit a lightweight U-Net-based deep architecture called Skinny for this task—it was originally employed for skin detection from color images, and benefits from a wider spatial context. We train multiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.

Original language	English
Title of host publication	Brainlesion
Subtitle of host publication	Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers
Editors	Alessandro Crimi, Spyridon Bakas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-14
Number of pages	12
ISBN (Print)	9783030720865
DOIs	https://doi.org/10.1007/978-3-030-72087-2_1
Publication status	Published - 2021
Event	6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 - Virtual, Online Duration: 4 Oct 2020 → 4 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12659 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
City	Virtual, Online
Period	4/10/20 → 4/10/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Brain tumor
Deep learning
Segmentation
U-Net

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-72087-2_1

Cite this

Tarasiewicz, T., Kawulok, M., & Nalepa, J. (2021). Lightweight U-Nets for Brain Tumor Segmentation. In A. Crimi, & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers (pp. 3-14). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12659 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-72087-2_1

Tarasiewicz, Tomasz ; Kawulok, Michal ; Nalepa, Jakub. / Lightweight U-Nets for Brain Tumor Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. editor / Alessandro Crimi ; Spyridon Bakas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Lightweight U-Nets for Brain Tumor Segmentation",

abstract = "Automated brain tumor segmentation is a vital topic due to its clinical applications. We propose to exploit a lightweight U-Net-based deep architecture called Skinny for this task{\textemdash}it was originally employed for skin detection from color images, and benefits from a wider spatial context. We train multiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.",

keywords = "Brain tumor, Deep learning, Segmentation, U-Net",

author = "Tomasz Tarasiewicz and Michal Kawulok and Jakub Nalepa",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",

year = "2021",

doi = "10.1007/978-3-030-72087-2\_1",

language = "English",

isbn = "9783030720865",

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",

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editor = "Alessandro Crimi and Spyridon Bakas",

booktitle = "Brainlesion",

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Tarasiewicz, T , Kawulok, M & Nalepa, J 2021, Lightweight U-Nets for Brain Tumor Segmentation. in A Crimi & S Bakas (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12659 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 3-14, 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, Virtual, Online, 4/10/20. https://doi.org/10.1007/978-3-030-72087-2_1

Lightweight U-Nets for Brain Tumor Segmentation. / Tarasiewicz, Tomasz ; Kawulok, Michal ; Nalepa, Jakub.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. ed. / Alessandro Crimi; Spyridon Bakas. Springer Science and Business Media Deutschland GmbH, 2021. p. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12659 LNCS).

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

TY - GEN

T1 - Lightweight U-Nets for Brain Tumor Segmentation

AU - Tarasiewicz, Tomasz

AU - Kawulok, Michal

AU - Nalepa, Jakub

PY - 2021

Y1 - 2021

N2 - Automated brain tumor segmentation is a vital topic due to its clinical applications. We propose to exploit a lightweight U-Net-based deep architecture called Skinny for this task—it was originally employed for skin detection from color images, and benefits from a wider spatial context. We train multiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.

AB - Automated brain tumor segmentation is a vital topic due to its clinical applications. We propose to exploit a lightweight U-Net-based deep architecture called Skinny for this task—it was originally employed for skin detection from color images, and benefits from a wider spatial context. We train multiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.

KW - Brain tumor

KW - Deep learning

KW - Segmentation

KW - U-Net

UR - https://www.scopus.com/pages/publications/85107390212

U2 - 10.1007/978-3-030-72087-2_1

DO - 10.1007/978-3-030-72087-2_1

M3 - Conference contribution

AN - SCOPUS:85107390212

SN - 9783030720865

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

SP - 3

EP - 14

BT - Brainlesion

A2 - Crimi, Alessandro

A2 - Bakas, Spyridon

PB - Springer Science and Business Media Deutschland GmbH

T2 - 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020

Y2 - 4 October 2020 through 4 October 2020

ER -

Tarasiewicz T , Kawulok M , Nalepa J. Lightweight U-Nets for Brain Tumor Segmentation. In Crimi A, Bakas S, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 3-14. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-72087-2_1

Lightweight U-Nets for Brain Tumor Segmentation

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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