An Improved Dense V-Network for Fast and Precise Segmentation of Left Atrium

Abstract

Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions; therefore, automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of the heart. Due to the small size of the left atrium with respect to the whole MRI volume, most of the current deep learning approaches are based on cropping or cascading networks. Dense V-Network is an encoder-decoder model designed for volumetric images by introducing a specialised dense feature stack to the standard V-Net model. A minor manipulation in parameters of the Dense V-Network can make it suitable for the fast and efficient segmentation of the left atrium. We present a brief review showing the ability of the Dense V-Network to deal with the issue of class imbalance and the unavailability of a large dataset of left atrium segmentation. We conclude that Dense V-Network can be tailored to left atrium MRI segmentation showing the accuracy that can surpass current methods, potentially supporting cardiac diagnosis and surgery.