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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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)

Original languageEnglish
Title of host publicationStatistical 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
EditorsKristin McLeod, Tommaso Mansi, Alistair Young, Kawal Rhode, Jichao Zhao, Shuo Li, Mihaela Pop, Maxime Sermesant
Place of PublicationCham
PublisherSpringer
Pages348-356
Number of pages9
ISBN (Electronic)978-3-030-12029-0
ISBN (Print)978-3-030-12028-3
DOIs
Publication statusPublished - 14 Feb 2019
Event9th 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 Sep 201816 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11395 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th 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
CountrySpain
CityGranada
Period16/09/1816/09/18

Fingerprint

Gadolinium
Magnetic resonance
Convolution
Magnetic resonance imaging
Segmentation
Enhancement
Neural Networks
Neural networks
Magnetic Resonance Image
Encoder
Pipelines
Pixels
Image Database
Pixel
Entire
Deep neural networks

Keywords

  • Convolutional neural networks
  • Image segmentation
  • Left atrium
  • U-Net

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). Cham: Springer. https://doi.org/10.1007/978-3-030-12029-0_38
de Vente, Coen ; Veta, Mitko ; Razeghi, Orod ; Niederer, Steven ; Pluim, Josien ; Rhode, Kawal ; Karim, Rashed. / 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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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)",
keywords = "Convolutional neural networks, Image segmentation, Left atrium, U-Net",
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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: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. 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; Niederer, Steven; Pluim, Josien; Rhode, Kawal; Karim, Rashed.

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 proceedingConference contributionAcademicpeer-review

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PB - Springer

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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)). https://doi.org/10.1007/978-3-030-12029-0_38