Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network

Abstract

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3D, especially from late gadolinium-enhanced (LGE)-MRIs, rely heavily on manual segmentation methods which are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3D is of high interest. We have therefore developed AtriaNet, a 16layer convolutional neural network (CNN), on 154 3D LGE-MRIs with a spatial resolution of 0.625 mm × 0.625 mm × 1.25 mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual pathway architecture that captures both the local atrial tissue geometry, and the global positional information of LA using 13 successive convolutions, and 3 further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3D LGE-MRI within one minute. Furthermore, benchmarking experiments showed that AtriaNet outperformed state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium respectively, and an inter-patient variance of <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm³ of the ground truths. Our proposed CNN was tested on the largest known dataset for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.

Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint‐atlas‐optimization

“…Xiong et al. used a patch‐based CNN for fully automatic LA segmentation, and achieved a Dice score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively . The method, however, was data intensive, employing a dataset of 154 AF patients.…”

“…Xiong et al used a patch-based CNN for fully automatic LA segmentation, and achieved a Dice score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively. 19 The method, however, was data intensive, employing a dataset of 154 AF patients. Mortazi et al used the encoder-decoder U-net in a multiview framework, and achieved a Dice value of 0.951 using leave-one-out cross validation on the same dataset (29 used for training and 1 used for testing).…”

“…In recent years, CNN shows great potential in segmentation of medical images and has been successfully applied to various anatomical objects . Xiong et al . proposed a patch‐based CNN for fully automatic LA segmentation.…”

“…In recent years, CNN shows great potential in segmentation of medical images and has been successfully applied to various anatomical objects. [15][16][17][18] Xiong et al 19 proposed a patch-based CNN for fully automatic LA segmentation. Mortazi et al proposed to segment the LA using the encoder-decoder architecture (U-NET 20 ) in a multiview framework and combined with an adaptive fusion strategy.…”

Segmentation of Multimodal Myocardial Images Using Shape-Transfer GAN

“…Recently proposed methods of LGE segmentation include model-based [1] and learning-based ones [2,3]. Zhuang et al (2018) used multivariate mixture model to describe the likelihood of multi-source images in a common space and model the motion shift of different slices with a rigid transformation.…”