Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data

Hafsa Moontari Ali; M. Shamim Kaiser; Mufti Mahmud

doi:10.1007/978-3-030-37078-7_14

Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data

Hafsa Moontari Ali, M. Shamim Kaiser^*, Mufti Mahmud

^*Corresponding author for this work

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

64 Scopus citations

Abstract

Extracting knowledge from digital images largely depends on how well the mining algorithms can focus on specific regions of the image. In multimodality image analysis, especially in multi-layer diagnostic images, identification of regions of interest is pivotal and this is mostly done through image segmentation. Reliable medical image analysis for error-free diagnosis requires efficient and accurate image segmentation mechanisms. With the advent of advanced machine learning methods, such as deep learning (DL), in intelligent diagnostics, the requirement of efficient and accurate image segmentation becomes crucial. Targeting the beginners, this paper starts with an overview of Convolutional Neural Network, the most widely used DL technique and its application to segment brain regions from Magnetic Resonance Imaging. It then provides a quantitative analysis of the reviewed techniques as well as a rich discussion on their performance. Towards the end, few open challenges are identified and promising future works related to medical image segmentation using DL are indicated.

Original language	English
Title of host publication	Brain Informatics - 12th International Conference, BI 2019, Proceedings
Editors	Peipeng Liang, Vinod Goel, Chunlei Shan
Publisher	Springer
Pages	136-146
Number of pages	11
ISBN (Print)	9783030370770
DOIs	https://doi.org/10.1007/978-3-030-37078-7_14
State	Published - 2019
Externally published	Yes
Event	12th International Conference on Brain Informatics, BI 2019 - Haikou, China Duration: 13 Dec 2019 → 15 Dec 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11976 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Brain Informatics, BI 2019
Country/Territory	China
City	Haikou
Period	13/12/19 → 15/12/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Brain imaging
Deep learning
MRI
Machine learning
Neuroimaging
Segmentation

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-37078-7_14

Cite this

Ali, H. M., Kaiser, M. S., & Mahmud, M. (2019). Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data. In P. Liang, V. Goel, & C. Shan (Eds.), Brain Informatics - 12th International Conference, BI 2019, Proceedings (pp. 136-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI). Springer. https://doi.org/10.1007/978-3-030-37078-7_14

Ali, Hafsa Moontari ; Kaiser, M. Shamim ; Mahmud, Mufti. / Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data. Brain Informatics - 12th International Conference, BI 2019, Proceedings. editor / Peipeng Liang ; Vinod Goel ; Chunlei Shan. Springer, 2019. pp. 136-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{137cdec92b0c4874b56f43b70da238ab,

title = "Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data",

abstract = "Extracting knowledge from digital images largely depends on how well the mining algorithms can focus on specific regions of the image. In multimodality image analysis, especially in multi-layer diagnostic images, identification of regions of interest is pivotal and this is mostly done through image segmentation. Reliable medical image analysis for error-free diagnosis requires efficient and accurate image segmentation mechanisms. With the advent of advanced machine learning methods, such as deep learning (DL), in intelligent diagnostics, the requirement of efficient and accurate image segmentation becomes crucial. Targeting the beginners, this paper starts with an overview of Convolutional Neural Network, the most widely used DL technique and its application to segment brain regions from Magnetic Resonance Imaging. It then provides a quantitative analysis of the reviewed techniques as well as a rich discussion on their performance. Towards the end, few open challenges are identified and promising future works related to medical image segmentation using DL are indicated.",

keywords = "Brain imaging, Deep learning, MRI, Machine learning, Neuroimaging, Segmentation",

author = "Ali, \{Hafsa Moontari\} and Kaiser, \{M. Shamim\} and Mufti Mahmud",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Brain Informatics, BI 2019 ; Conference date: 13-12-2019 Through 15-12-2019",

year = "2019",

doi = "10.1007/978-3-030-37078-7\_14",

language = "English",

isbn = "9783030370770",

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

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editor = "Peipeng Liang and Vinod Goel and Chunlei Shan",

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Ali, HM, Kaiser, MS & Mahmud, M 2019, Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data. in P Liang, V Goel & C Shan (eds), Brain Informatics - 12th International Conference, BI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11976 LNAI, Springer, pp. 136-146, 12th International Conference on Brain Informatics, BI 2019, Haikou, China, 13/12/19. https://doi.org/10.1007/978-3-030-37078-7_14

Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data. / Ali, Hafsa Moontari; Kaiser, M. Shamim; Mahmud, Mufti.
Brain Informatics - 12th International Conference, BI 2019, Proceedings. ed. / Peipeng Liang; Vinod Goel; Chunlei Shan. Springer, 2019. p. 136-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI).

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

TY - GEN

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AU - Ali, Hafsa Moontari

AU - Kaiser, M. Shamim

AU - Mahmud, Mufti

PY - 2019

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N2 - Extracting knowledge from digital images largely depends on how well the mining algorithms can focus on specific regions of the image. In multimodality image analysis, especially in multi-layer diagnostic images, identification of regions of interest is pivotal and this is mostly done through image segmentation. Reliable medical image analysis for error-free diagnosis requires efficient and accurate image segmentation mechanisms. With the advent of advanced machine learning methods, such as deep learning (DL), in intelligent diagnostics, the requirement of efficient and accurate image segmentation becomes crucial. Targeting the beginners, this paper starts with an overview of Convolutional Neural Network, the most widely used DL technique and its application to segment brain regions from Magnetic Resonance Imaging. It then provides a quantitative analysis of the reviewed techniques as well as a rich discussion on their performance. Towards the end, few open challenges are identified and promising future works related to medical image segmentation using DL are indicated.

AB - Extracting knowledge from digital images largely depends on how well the mining algorithms can focus on specific regions of the image. In multimodality image analysis, especially in multi-layer diagnostic images, identification of regions of interest is pivotal and this is mostly done through image segmentation. Reliable medical image analysis for error-free diagnosis requires efficient and accurate image segmentation mechanisms. With the advent of advanced machine learning methods, such as deep learning (DL), in intelligent diagnostics, the requirement of efficient and accurate image segmentation becomes crucial. Targeting the beginners, this paper starts with an overview of Convolutional Neural Network, the most widely used DL technique and its application to segment brain regions from Magnetic Resonance Imaging. It then provides a quantitative analysis of the reviewed techniques as well as a rich discussion on their performance. Towards the end, few open challenges are identified and promising future works related to medical image segmentation using DL are indicated.

KW - Brain imaging

KW - Deep learning

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KW - Neuroimaging

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

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BT - Brain Informatics - 12th International Conference, BI 2019, Proceedings

A2 - Liang, Peipeng

A2 - Goel, Vinod

A2 - Shan, Chunlei

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T2 - 12th International Conference on Brain Informatics, BI 2019

Y2 - 13 December 2019 through 15 December 2019

ER -

Ali HM, Kaiser MS, Mahmud M. Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data. In Liang P, Goel V, Shan C, editors, Brain Informatics - 12th International Conference, BI 2019, Proceedings. Springer. 2019. p. 136-146. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-37078-7_14

Application of Convolutional Neural Network in Segmenting Brain Regions from MRI Data

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this