Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images

Coen de Vente; Mitko Veta; Orod Razeghi; Steven Niederer; Josien Pluim; Kawal Rhode; Rashed Karim

doi:10.1007/978-3-030-12029-0_38

Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images

Coen de Vente, Mitko Veta, Orod Razeghi, Steven Niederer, Josien Pluim, Kawal Rhode, Rashed Karim^*

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

This paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers
Editors	Kristin McLeod, Tommaso Mansi, Alistair Young, Kawal Rhode, Jichao Zhao, Shuo Li, Mihaela Pop, Maxime Sermesant
Publisher	Springer Verlag
Pages	348-356
Number of pages	9
ISBN (Print)	9783030120283
DOIs	https://doi.org/10.1007/978-3-030-12029-0_38
Publication status	E-pub ahead of print - 14 Feb 2019
Event	9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 16 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11395 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/2018 → 16/09/2018

Keywords

Convolutional neural networks
Image segmentation
Left atrium
U-Net

Access to Document

10.1007/978-3-030-12029-0_38

Cite this

de Vente, C., Veta, M., Razeghi, O., Niederer, S., Pluim, J., Rhode, K., & Karim, R. (2019). Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. In K. McLeod, T. Mansi, A. Young, K. Rhode, J. Zhao, S. Li, M. Pop, & M. Sermesant (Eds.), Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers (pp. 348-356). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11395 LNCS). Springer Verlag. Advance online publication. https://doi.org/10.1007/978-3-030-12029-0_38

de Vente, Coen ; Veta, Mitko ; Razeghi, Orod et al. / Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. editor / Kristin McLeod ; Tommaso Mansi ; Alistair Young ; Kawal Rhode ; Jichao Zhao ; Shuo Li ; Mihaela Pop ; Maxime Sermesant. Springer Verlag, 2019. pp. 348-356 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images",

abstract = "This paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)",

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doi = "10.1007/978-3-030-12029-0_38",

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note = "9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",

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de Vente, C, Veta, M, Razeghi, O, Niederer, S, Pluim, J, Rhode, K & Karim, R 2019, Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. in K McLeod, T Mansi, A Young, K Rhode, J Zhao, S Li, M Pop & M Sermesant (eds), Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11395 LNCS, Springer Verlag, pp. 348-356, 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/2018. https://doi.org/10.1007/978-3-030-12029-0_38

Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. / de Vente, Coen; Veta, Mitko; Razeghi, Orod et al.
Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. ed. / Kristin McLeod; Tommaso Mansi; Alistair Young; Kawal Rhode; Jichao Zhao; Shuo Li; Mihaela Pop; Maxime Sermesant. Springer Verlag, 2019. p. 348-356 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11395 LNCS).

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

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T1 - Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images

AU - de Vente, Coen

AU - Veta, Mitko

AU - Razeghi, Orod

AU - Niederer, Steven

AU - Pluim, Josien

AU - Rhode, Kawal

AU - Karim, Rashed

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N2 - This paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)

AB - This paper introduces a left atrial segmentation pipeline that utilises a deep neural network for learning segmentations of the LA from Gadolinium enhancement magnetic resonance images (GE-MRI). The trainable fully-convolutional neural network consists of an encoder network and a corresponding decoder network followed by a pixel-wise classification layer. The entire network has 17 convolutional layers, with the encoder network containing 5 convolutional layers, and the decoder network containing 11 convolution layers with 1 additional convolution layer in between. The training image database consisted of manually annotated GE-MRI images ((Formula Presented)

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SN - 9783030120283

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

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BT - Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers

A2 - McLeod, Kristin

A2 - Mansi, Tommaso

A2 - Young, Alistair

A2 - Rhode, Kawal

A2 - Zhao, Jichao

A2 - Li, Shuo

A2 - Pop, Mihaela

A2 - Sermesant, Maxime

PB - Springer Verlag

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ER -

de Vente C, Veta M, Razeghi O, Niederer S, Pluim J, Rhode K et al. Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images. In McLeod K, Mansi T, Young A, Rhode K, Zhao J, Li S, Pop M, Sermesant M, editors, Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges - 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Revised Selected Papers. Springer Verlag. 2019. p. 348-356. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2019 Feb 14. doi: 10.1007/978-3-030-12029-0_38

Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images

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