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

Medical Image Analysis

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

7 Citations (Scopus)

4 Downloads (Pure)

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
Place of Publication	Cham
Publisher	Springer
Pages	348-356
Number of pages	9
ISBN (Electronic)	978-3-030-12029-0
ISBN (Print)	978-3-030-12028-3
DOIs	https://doi.org/10.1007/978-3-030-12029-0_38
Publication status	Published - 14 Feb 2019
Event	9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) : Atrial Segmentation and LV Quantification Challenges - Granada, Spain Duration: 16 Sept 2018 → 16 Sept 2018 Conference number: 9

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 (STACOM 2018)
Abbreviated title	STACOM 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18
Other	held in conjunction with the 21st Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018

Funding

Acknowledgements. This research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. This work was also supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Thanks to NVIDIA for donating a GPU for deep learning experiments.

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. 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. Cham : Springer, 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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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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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, Cham, pp. 348-356, 9th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2018) , Granada, Spain, 16/09/18. 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. Cham: Springer, 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 contribution › Academic › peer-review

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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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A2 - Zhao, Jichao

A2 - Li, Shuo

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A2 - Sermesant, Maxime

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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. Cham: Springer. 2019. p. 348-356. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). 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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