Two-Stage 2D CNN for Automatic Atrial Segmentation from LGE-MRIs

Abstract

Atrial fibrillation (AF) is the most common sustained heart rhythm disturbance and a leading cause of hospitalization, heart failure and stroke. In the current medical practice, atrial segmentation from medical images for clinical diagnosis and treatment, is a labor-intensive and error-prone manual process. The atrial segmentation challenge held in conjunction with the 2018 the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) conference and Statistical Atlases and Computational Modelling of the Heart (STACOM), offered the opportunity to develop reliable approaches to automatically annotate and perform segmentation of the left atrial (LA) chamber using the largest available 3D late gadolinium-enhanced MRI (LGE-MRI) dataset with 154 3D LGE-MRIs and labels. For this challenge, 11 out the 27 contestants achieved more than 90% Dice score accuracy, however, a critical question remains as which is the optimal approach for LA segmentation. In this paper, we propose a two-stage 2D fully convolutional neural network with extensive data augmentation and achieves a superior segmentation accuracy with a Dice score of 93.7% using the same dataset and conditions as for the atrial segmentation challenge. Thus, our approach outperforms the methods proposed in the atrial segmentation challenge while employing less computational resources than the challenge winning method.