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 ($n=75$). 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.