Echocardiography (echo) is a skilled technical procedure that depends on the experience of the operator. The aim of this paper is to reduce user variability in data acquisition by automatically computing a score of echo quality for operator feedback. To do this, a deep convolutional neural network model, trained on a large set of samples, was developed for scoring apical four-chamber (A4C) echo. In this paper, 6,916 end-systolic echo images were manually studied by an expert cardiologist and were assigned a score between 0 (not acceptable) and 5 (excellent). The images were divided into two independent training-validation and test sets. The network architecture and its parameters were based on the stochastic approach of the particle swarm optimization on the training-validation data. The mean absolute error between the scores from the ultimately trained model and the expert’s manual scores was 0.71 ± 0.58. The reported error was comparable to the measured intra-rater reliability. The learned features of the network were visually interpretable and could be mapped to the anatomy of the heart in the A4C echo, giving confidence in the training result. The computation time for the proposed network architecture, running on a graphics processing unit, was less than 10 ms per frame, sufficient for real-time deployment. The proposed approach has the potential to facilitate the widespread use of echo at the point-of-care and enable early and timely diagnosis and treatment. Finally, the approach did not use any specific assumptions about the A4C echo, so it could be generalizable to other standard echo views.

Correction to “Automatic Quality Assessment of Echocardiograms Using Convolutional Neural Networks: Feasibility on the Apical Four-Chamber View”

Echocardiography (echo) is a clinical imaging technique which is highly dependent on operator experience. We aim to reduce operator variability in data acquisition by automatically computing an echo quality score for real-time feedback. We achieve this with a deep neural network model, with convolutional layers to extract hierarchical features from the input echo cine and recurrent layers to leverage the sequential information in the echo cine loop. Using data from 509 separate patient studies, containing 2,450 echo cines across five standard echo imaging planes, we achieved a mean quality score accuracy of 85\(\%\) compared to the gold-standard score assigned by experienced echosonographers. The proposed approach calculates the quality of a given 20 frame echo sequence within 10 ms, sufficient for real-time deployment.

Quality Assessment of Echocardiographic Cine Using Recurrent Neural Networks: Feasibility on Five Standard View Planes

Echocardiography (echo) is a critical tool in diagnosing various cardiovascular diseases. Despite its diagnostic and prognostic value, interpretation and analysis of echo images are still widely performed manually by echocardiographers. A plethora of algorithms has been proposed to analyze medical ultrasound data using signal processing and machine learning techniques. These algorithms provided opportunities for developing automated echo analysis and interpretation systems. The automated approach can significantly assist in decreasing the variability and burden associated with manual image measurements. In this paper, we review the state-of-the-art automatic methods for analyzing echocardiography data. Particularly, we comprehensively and systematically review existing methods of four major tasks: echo quality assessment, view classification, boundary segmentation, and disease diagnosis. Our review covers three echo imaging modes, which are B-mode, M-mode, and Doppler. We also discuss the challenges and limitations of current methods and outline the most pressing directions for future research. In summary, this review presents the current status of automatic echo analysis and discusses the challenges that need to be addressed to obtain robust systems suitable for efficient use in clinical settings or point-of-care testing.

Harnessing Machine Intelligence in Automatic Echocardiogram Analysis: Current Status, Limitations, and Future Directions

Echocardiography (echo) is the most common test for diagnosis and management of patients with cardiac condi- tions. While most medical imaging modalities benefit from a relatively automated procedure, this is not the case for echo and the quality of the final echo view depends on the competency and experience of the sonographer. It is not uncommon that the sonographer does not have adequate experience to adjust the transducer and acquire a high quality echo, which may further affect the clinical diagnosis. In this work, we aim to aid the operator during image acquisition by automatically assessing the quality of the echo and generating the Automatic Echo Score (AES). This quality assessment method is based on a deep convolutional neural network, trained in an end-to-end fashion on a large dataset of apical four-chamber (A4C) echo images. For this project, an expert car- diologist went through 2,904 A4C images obtained from independent studies and assessed their condition based on a 6-scale grading system. The scores assigned by the expert ranged from 0 to 5. The distribution of scores among the 6 levels were almost uniform. The network was then trained on 80% of the data (2,345 samples). The average absolute error of the trained model in calculating the AES was 0.8 ± 0:72. The computation time of
the GPU implementation of the neural network was estimated at 5 ms per frame, which is sufficient for real-time
deployment.

Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

https://doi.org/10.1016/j.media.2022.102438

Real-time echocardiography image analysis and quantification of cardiac indices

This paper presents a method for automatically estimating the quality of Parasternal Long AXis (PLAX) B-mode echocardiograms. The purpose of the algorithm is to provide live feedback to the user on the quality of the acquired image. The proposed approach uses Generalized Hough Transform to compare the structures derived from the incoming image to a representative atlas, thereby providing a quality metric (PQM). On 133 PLAX images from 35 patients, we show: 1) PQM has high correlation with manual ratings from an expert echocardiographer 2) PQM has high correlation with contrast-to-noise ratio, a traditional indicator of image quality 3) on images with high PQM, error in automatic septal wall thickness measurement is low, and vice versa.

/pdf/quality-metric-for-parasternal-long-axis-b-mode-1ypb67hcov.pdf

Quality metric for parasternal long axis b-mode echocardiograms

Automated robust segmentation of intra-ventricular septum (IVS) from B-mode echocardiographic images is an enabler for early quantification of cardiac disease. Segmentation of septum from ultrasound images is very challenging due to variations in intensity/contrast in and around the septum, speckle noise and non-rigid shape variations of the septum boundary. In this work, we effectively address these challenges using an approach that merges novel computer vision ideas with physiological markers present in cardiac scans. Specifically, we contribute towards the following: 1) A novel 1-D active contour segmentation approach that utilizes non-local (NL) temporal cues, 2) Robust initialization of the active contour framework, based on NL-means de-noising, and MRF based clustering that incorporates physiological cues. We validate our claims using cardiac measurement results on ~30 cardiac scan videos (~2000 ultrasound frames in total). Our method is fully automatic and near real time ( 0.1sec/frame) implementation.

/pdf/automated-intraventricular-septum-segmentation-using-non-4tjinzmpuu.pdf

Automated intraventricular septum segmentation using non-local spatio-temporal priors

In this paper, we present a robust algorithm to segment the posterior wall region and estimate wall thickness from parasternal long axis(PLAX) view cardiac US B-mode images. Posterior wall thickness (PWd), Septal wall thickness (SWTd) and Left ventricular Internal diameter(LVId) are used to detect and measure the extent of Left Ventricular Hypertrophy (LVH). Manual measurements of PWd suffers from large inter and intra observer variability due to weak endocardial boundary intertangled with speckle and poor contrast, movement of the fibrous structures like the chordae,papillary muscles and posterior mitral leaflet. The proposed algorithm seeks to address some of these issues by automating the measurement algorithm. The algorithm initially detects epicardial boundary by pericardium detection and later segments the endocardial boundary by a 1D active contour evolution. We have designed the algorithm on a pilot data set of 42 images and validated on 88 patient data sets.The measurement values are in excellent agreement with expert measurements with error = 2.06mm ± 1.5mm.

Automated posteriorwall thickness measurement from B-mode ultrasound

When imaging the heart, using a 2D ultrasound probe, different views can manifest depending on the location and angulations of the probe. Some of these views have been labeled as standard views, due to the presentation and ease of assessment of key cardiac structures in them. We present an approach for automatic recognition and classification of these standard views, as a potential enabler for automated measurements or detection of noise — all without a human in the loop. We present an approach for view classification, Spatial Pyramid Histogram of Words which successfully models the appearance and shape distributions of object class. We demonstrate the effectiveness of this technique for the task of discrimination between the B-mode Parasternal Long Axis (PLAX) and the Short Axis (SAX) echocardiograms. For this task, our method shows a classification accuracy of 98.3% on an exhaustive database of 703 ultrasound images.

Satish Govind

Papers

Quality metric for parasternal long axis b-mode echocardiograms

Automated intraventricular septum segmentation using non-local spatio-temporal priors

Automated posteriorwall thickness measurement from B-mode ultrasound

Echocardiogram view classification with appearance and spatial distributions