@inproceedings{752ab73f055a48e3bff76728afe24cf9,
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",
author = "{de Vente}, Coen and Mitko Veta and Orod Razeghi and Steven Niederer and Josien Pluim and Kawal Rhode and Rashed Karim",
note = "Funding Information: Acknowledgements. This research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy{\textquoteright}s and St Thomas{\textquoteright} NHS Foundation Trust and King{\textquoteright}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. Funding Information: This research was supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at Guy{\textquoteright}s and St Thomas{\textquoteright} NHS Foundation Trust and King{\textquoteright}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. Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 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",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-12029-0_38",
language = "English",
isbn = "9783030120283",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "348--356",
editor = "Kristin McLeod and Tommaso Mansi and Alistair Young and Kawal Rhode and Jichao Zhao and Shuo Li and Mihaela Pop and Maxime Sermesant",
booktitle = "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",
address = "Germany",
}