Abstract
Background:
Most quantitative features in analyzing echocardiography images are elicited from the shape of different parts of the heart. One of the challenging tasks in this area is detecting the border between the left ventricle and its wall. Segmentation that is a process to extract the shape of objects in an image is a way to have a better observation of epicardial and endocardial parts of the left ventricle. Today, manual segmentation is performed by expert radiologists in most cases, but there is some research in the field of automatic echocardiography image segmentation by the use of image processing and computer vision methods. Automatic segmentation is desired because it is more accurate and less operator-dependent. It leads to further quantifications such as the measurement of LV volumes, ejection fraction, myocardial volumes, and thickness. It can be also used for evaluating myocardial perfusion by analyzing myocardial intensity changes over time. Due to the intrinsic limitations of echo imaging such as low image intensity, contrast traditional segmentation methods such as edge-based and region-based image processing algorithms could not be accurate enough to overcome the segmentation complexities. Deep learning that is a branch in the computer vision area has been shown to outperform the image processing methods in many tasks.Objectives:
In this study, we used a novel image segmentation neural network (Unet) first introduced in 2014 to segment the myocardium in the left ventricle in 2D four-chamber view echocardiography images.Methods:
The dataset used in this research was the public echocardiography image dataset published in CAMUS (Cardiac Acquisitions for Multi-structure Ultrasound Segmentation) Challenge. The data contained four-chamber view end-systole and end-diastole frames from 450 patients. We used Unet architecture for the segmentation task. Unet is a kind of pyramidal network with encoding and decoding paths. The encoder or contraction path was used to capture the context and features in the image. The second path was the symmetric expanding or decoder path used to enable precise localization. The whole task of the network was to classify each pixel in the image to the background or epicardium classes.Results:
Five-fold cross-validation was used to report the accuracy metrics for the automatic segmentation task. The data were split into the train and test sets several times to evaluate the performance of the neural network. The test and train sets contained 90 and 360 images, respectively. Dice coefficient, Hausdorff distance, and mean absolute distance (MAD) were used to evaluate the accuracy of the method in echocardiography images. The calculated metrics included a dice coefficient of 90%, Hausdorff distance of 5.01, and MAD of 0.91 for the training set and dice coefficient of 83.14%, Hausdorff distance of 6.55, and MAD of 1.47 for the test set.Conclusion:
We used a novel neural network architecture for the myocardial segmentation task in 2D four-chamber view echocardiography images. We showed that deep learning algorithm automated segmentation can be an accurate alternative to extract the geometric features from images. Using this method can lead the operator to better analyze for LV and myocardial measurements. An approach for future work is expanding the automation to the measurement level from the segmented part.Copyright
Copyright © 2019, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.