Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network

TL;DR

This paper presents an automated fully convolutional network-based method for segmenting the left ventricle in cardiac MR images, addressing challenges like noise and boundary inaccuracies to improve clinical diagnosis support.

Contribution

The study introduces a novel automated segmentation approach combining ROI extraction, FCN training, and post-processing, achieving high accuracy on cardiac MR images.

Findings

Dice score of 87.24% on York dataset

Effective ROI extraction improves segmentation accuracy

Post-processing enhances boundary delineation

Abstract

Medical image analysis, especially segmenting a specific organ, has an important role in developing clinical decision support systems. In cardiac magnetic resonance (MR) imaging, segmenting the left and right ventricles helps physicians diagnose different heart abnormalities. There are challenges for this task, including the intensity and shape similarity between left ventricle and other organs, inaccurate boundaries and presence of noise in most of the images. In this paper we propose an automated method for segmenting the left ventricle in cardiac MR images. We first automatically extract the region of interest, and then employ it as an input of a fully convolutional network. We train the network accurately despite the small number of left ventricle pixels in comparison with the whole image. Thresholding on the output map of the fully convolutional network and selection of regions based on their roundness are performed in our proposed post-processing phase. The Dice score of our method reaches 87.24% by applying this algorithm on the York dataset of heart images.