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Published in:
2019 | OriginalPaper | Chapter
Convolutional Neural Networks for Segmentation of the Left Atrium from Gadolinium-Enhancement MRI Images
Authors : Coen de Vente, Mitko Veta, Orod Razeghi, Steven Niederer, Josien Pluim, Kawal Rhode, Rashed Karim
Published in: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
Publisher: Springer International Publishing
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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 (\(n=75\)). Dice scores of \(0.87 \pm 0.07\) and \(0.80 \pm 0.12\) were achieved on our test set (\(n=25\)) and a multi-centre independent set using transfer learning, respectively. On the test set that was provided by the challenge (\(n=54\)), a Dice score of 0.897 was achieved. We experimentally demonstrated the robustness of the proposed method as a segmentation pipeline for potential use in clinical research.