Abstract

Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Several convolutional neural network (CNN) architectures have been proposed to segment the heart chambers from cardiac cine MR images. Here we propose a multi-task learning (MTL)-based regularization framework for cardiac MR image segmentation. The network is trained to perform the main task of semantic segmentation, along with a simultaneous, auxiliary task of pixel-wise distance map regression. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures on two publicly available cardiac cine MRI datasets, obtaining average dice coefficient of 0.84±0.03 and 0.91±0.04, respectively. Furthermore, we also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56±0.28 to 0.80±0.14.

Index Terms—Magnetic resonance imaging (MRI), Heart Segmentation, Convolutional Neural network, Regularization

Introduction

MAGNETIC Resonance Imaging (MRI) is the standardof-care imaging modality for non-invasive cardiac diagnosis, due to its high contrast sensitivity to soft tissue, good image quality, and lack of exposure to ionizing radiation. Cine cardiac MRI enables the acquisition of high resolution twodimensional (2D) anatomical images of the heart throughout the cardiac cycle, capturing the full cardiac dynamics via multiple 2D + time short axis acquisitions spanning the whole heart. Segmentation of the heart structures from these images enables measurement of important cardiac diagnostic indices such as myocardial mass and thickness, left/right ventricle (LV/RV) volumes and ejection fraction. Furthermore, highquality personalized heart models can be generated for cardiac morphology assessment, treatment planning, as well as, precise localization of pathologies during an image-guided intervention. Manual delineation is the standard cardiac image segmentation approach, which is not only time consuming, but also susceptible to high inter- and intra-observer variability. Hence, there is a critical need for semi-/fully-automatic methods for cardiac cine MRI segmentation. However, the MR imaging artifacts such as bias fields, respiratory motion, and intensity inhomogeneity and fuzziness, render the segmentation of heart structures challenging. Fig shows a reference segmentation and the results of our automatic segmentation method.

Authors:Shusil Dangi, Cristian A. Linte, Senior Member, IEEE, and Ziv Yaniv, Senior Member, IEEE

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