Hybrid labels for brain tumor segmentation

Parvez Ahmad; Saqib Qamar; Seyed Raein Hashemi; Linlin Shen

doi:10.1007/978-3-030-46643-5_15

Hybrid labels for brain tumor segmentation

Parvez Ahmad^*
, Saqib Qamar
, Seyed Raein Hashemi
, Linlin Shen

^*Corresponding author for this work

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

17 Citations (Scopus)

Abstract

The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent results against classical machine learning algorithms. In this paper, we present a unique approach to incorporate contexual information from multiple brain MRI labels. To address the problems of brain tumor segmentation, we implement combined strategies of residual-dense connections, multiple rates of an atrous convolutional layer on popular 3D U-Net architecture. To train and validate our proposed algorithm, we used BRATS 2019 different datasets. The results are promising on the different evaluation metrics.

Original language	English
Title of host publication	Brainlesion
Subtitle of host publication	Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
Editors	Alessandro Crimi, Spyridon Bakas
Publisher	Springer
Pages	158-166
Number of pages	9
ISBN (Print)	9783030466428
DOIs	https://doi.org/10.1007/978-3-030-46643-5_15
Publication status	Published - 2020
Externally published	Yes
Event	5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 17 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11993 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	17/10/19 → 17/10/19

UN SDGs

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

SDG 3 Good Health and Well-being

Free Keywords

Atrous rates
Brain tumor segmentation
Convolutional neural networks
Deep learning
Residual-dense connections

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-46643-5_15

Cite this

Ahmad, P., Qamar, S., Hashemi, S. R., & Shen, L. (2020). Hybrid labels for brain tumor segmentation. In A. Crimi, & S. Bakas (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers (pp. 158-166). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11993 LNCS). Springer. https://doi.org/10.1007/978-3-030-46643-5_15

Ahmad, Parvez ; Qamar, Saqib ; Hashemi, Seyed Raein et al. / Hybrid labels for brain tumor segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. editor / Alessandro Crimi ; Spyridon Bakas. Springer, 2020. pp. 158-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{68fc80d626514742b38d7f4ff552bf20,

title = "Hybrid labels for brain tumor segmentation",

abstract = "The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent results against classical machine learning algorithms. In this paper, we present a unique approach to incorporate contexual information from multiple brain MRI labels. To address the problems of brain tumor segmentation, we implement combined strategies of residual-dense connections, multiple rates of an atrous convolutional layer on popular 3D U-Net architecture. To train and validate our proposed algorithm, we used BRATS 2019 different datasets. The results are promising on the different evaluation metrics.",

keywords = "Atrous rates, Brain tumor segmentation, Convolutional neural networks, Deep learning, Residual-dense connections",

author = "Parvez Ahmad and Saqib Qamar and Hashemi, \{Seyed Raein\} and Linlin Shen",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",

year = "2020",

doi = "10.1007/978-3-030-46643-5\_15",

language = "English",

isbn = "9783030466428",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "158--166",

editor = "Alessandro Crimi and Spyridon Bakas",

booktitle = "Brainlesion",

}

Ahmad, P, Qamar, S, Hashemi, SR & Shen, L 2020, Hybrid labels for brain tumor segmentation. in A Crimi & S Bakas (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11993 LNCS, Springer, pp. 158-166, 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019, Shenzhen, China, 17/10/19. https://doi.org/10.1007/978-3-030-46643-5_15

Hybrid labels for brain tumor segmentation. / Ahmad, Parvez; Qamar, Saqib; Hashemi, Seyed Raein et al.
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. ed. / Alessandro Crimi; Spyridon Bakas. Springer, 2020. p. 158-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11993 LNCS).

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

TY - GEN

T1 - Hybrid labels for brain tumor segmentation

AU - Ahmad, Parvez

AU - Qamar, Saqib

AU - Hashemi, Seyed Raein

AU - Shen, Linlin

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent results against classical machine learning algorithms. In this paper, we present a unique approach to incorporate contexual information from multiple brain MRI labels. To address the problems of brain tumor segmentation, we implement combined strategies of residual-dense connections, multiple rates of an atrous convolutional layer on popular 3D U-Net architecture. To train and validate our proposed algorithm, we used BRATS 2019 different datasets. The results are promising on the different evaluation metrics.

AB - The accurate automatic segmentation of brain tumors enhances the probability of survival rate. Convolutional Neural Network (CNN) is a popular automatic approach for image evaluations. CNN provides excellent results against classical machine learning algorithms. In this paper, we present a unique approach to incorporate contexual information from multiple brain MRI labels. To address the problems of brain tumor segmentation, we implement combined strategies of residual-dense connections, multiple rates of an atrous convolutional layer on popular 3D U-Net architecture. To train and validate our proposed algorithm, we used BRATS 2019 different datasets. The results are promising on the different evaluation metrics.

KW - Atrous rates

KW - Brain tumor segmentation

KW - Convolutional neural networks

KW - Deep learning

KW - Residual-dense connections

UR - https://www.scopus.com/pages/publications/85085485001

U2 - 10.1007/978-3-030-46643-5_15

DO - 10.1007/978-3-030-46643-5_15

M3 - Conference contribution

AN - SCOPUS:85085485001

SN - 9783030466428

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

SP - 158

EP - 166

BT - Brainlesion

A2 - Crimi, Alessandro

A2 - Bakas, Spyridon

PB - Springer

T2 - 5th International MICCAI Brainlesion Workshop, BrainLes 2019, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, MICCAI 2019

Y2 - 17 October 2019 through 17 October 2019

ER -

Ahmad P, Qamar S, Hashemi SR, Shen L. Hybrid labels for brain tumor segmentation. In Crimi A, Bakas S, editors, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers. Springer. 2020. p. 158-166. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-46643-5_15

Hybrid labels for brain tumor segmentation

Abstract

Publication series

Conference

UN SDGs

Free Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this