A Unified Approach for Comprehensive Analysis of Various Spectral and Tissue Doppler Echocardiography

Doppler echocardiography offers critical insights into cardiac function and phases by quantifying blood flow velocities and evaluating myocardial motion. However, previous methods for automating Doppler analysis, ranging from initial signal processing techniques to advanced deep learning approaches, have been constrained by their reliance on electrocardiogram (ECG) data and their inability to process Doppler views collectively. We introduce a novel unified framework using a convolutional neural network for comprehensive analysis of spectral and tissue Doppler echocardiography images that combines automatic measurements and end-diastole (ED) detection into a singular method. The network automatically recognizes key features across various Doppler views, with novel Doppler shape embedding and anti-aliasing modules enhancing interpretation and ensuring consistent analysis. Empirical results indicate a consistent outperformance in performance metrics, including dice similarity coefficients (DSC) and intersection over union (IoU). The proposed framework demonstrates strong agreement with clinicians in Doppler automatic measurements and competitive performance in ED detection

Echocardiographic View Classification with Integrated Out-of-Distribution Detection for Enhanced Automatic Echocardiographic Analysis

In the rapidly evolving field of automatic echocardiographic analysis and interpretation, automatic view classification is a critical yet challenging task, owing to the inherent complexity and variability of echocardiographic data. This study presents ECHOcardiography VIew Classification with Out-of-Distribution dEtection (ECHO-VICODE), a novel deep learning-based framework that effectively addresses this challenge by training to classify 31 classes, surpassing previous studies and demonstrating its capacity to handle a wide range of echocardiographic views. Furthermore, ECHO-VICODE incorporates an integrated out-of-distribution (OOD) detection function, leveraging the relative Mahalanobis distance to effectively identify 'near-OOD' instances commonly encountered in echocardiographic data. Through extensive experimentation, we demonstrated the outstanding performance of ECHO-VICODE in terms of view classification and OOD detection, significantly reducing the potential for errors in echocardiographic analyses. This pioneering study significantly advances the domain of automated echocardiography analysis and exhibits promising prospects for substantial applications in extensive clinical research and practice

Self supervised convolutional kernel based handcrafted feature harmonization: Enhanced left ventricle hypertension disease phenotyping on echocardiography

Radiomics, a medical imaging technique, extracts quantitative handcrafted features from images to predict diseases. Harmonization in those features ensures consistent feature extraction across various imaging devices and protocols. Methods for harmonization include standardized imaging protocols, statistical adjustments, and evaluating feature robustness. Myocardial diseases such as Left Ventricular Hypertrophy (LVH) and Hypertensive Heart Disease (HHD) are diagnosed via echocardiography, but variable imaging settings pose challenges. Harmonization techniques are crucial for applying handcrafted features in disease diagnosis in such scenario. Self-supervised learning (SSL) enhances data understanding within limited datasets and adapts to diverse data settings. ConvNeXt-V2 integrates convolutional layers into SSL, displaying superior performance in various tasks. This study focuses on convolutional filters within SSL, using them as preprocessing to convert images into feature maps for handcrafted feature harmonization. Our proposed method excelled in harmonization evaluation and exhibited superior LVH classification performance compared to existing methods.Comment: 11 pages, 7 figure

Legislative Documents

Also, variously referred to as: Senate bills; Senate documents; Senate legislative documents; legislative documents; and General Court documents

Automatic Aortic Valve Landmark Localization in Coronary CT Angiography Using Colonial Walk

The zipped file contains the visual C project for the work "Automatic Aortic Valve Landmark Localization in Coronary CT Angiography Using Colonial Walk", as accepted in the journal of PLOS ONE.<br><br>Additional library dependency:<div>OpenCV 2.4.9</div

View customization for manually marking the landmarks and diagnosing.

<p>(a) Original view. (b) Customized view. Red, green and blue lines indicate the X, Y and Z axes, respectively. Transverse plane is carefully rotated about X an Y axes to have a view parallel to the aortic annulus because the aorta pose is not clear in the original view.</p

The dependency of the average localization error (in mm) on the step length (in voxels) and the number of steps.

<p>The blue and red dots in (a), (b) and (c) are the initial and the target ground truth positions, respectively. The walker fails to reach the target because of too small step length. Scattered movement is noticed for a very big step length. Relatively smooth movement is noticed for an optimal step length. (d) Dependency of the average localization error on the total number of steps for different step lengths.</p

The aortic valve anatomy.

<p>(a) Rendered CT volume after thresholding to visualize the aortic valve. (b) An enlarged view of the aortic valve. The blue, green and red dots refer to the coronary ostia, the aortic hinges, and the aortic commissures, respectively. The commissure between the right-coronary and non-coronary hinges and the commissure between the left-coronary and non-coronary hinges are occluded in this view.</p

A single walk towards a target point in a 3D volume.

<p>The red dot is the target ground truth point. (a) The blue arrows refer to the learned unit directions to the ground truth at each voxel. (b) A walker starts from the blue point (i.e., the initial voxel) and updates to its next position taking a step towards the learned direction at the current position. After a certain steps, it starts moving around the ground truth point. The expectation of the step positions gives the target position.</p