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

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

Improving the Reproducibility of Computed Tomography Radiomic Features Using an Enhanced Hierarchical Feature Synthesis Network

Radiomics has gained popularity as a quantitative analysis method for medical images. However, computed tomography (CT) scans are performed using various parameters, such as X-ray dose and reconstruction kernels, which is a fundamental reason for the lack of reproducibility of radiomic features. This study evaluated whether the proposed network improves the reproducibility of radiomic features across various CT protocols and reconstruction kernels. We set five CT scan protocols and two reconstruction kernels to create various noise settings for the obtained CT images with an abdominal phantom. We developed an enhanced hierarchical feature synthesis (EHFS) network to improve the reproducibility of radiomic features across various CT protocols and reconstruction kernels. Eight hundred and nineteen radiomic features were extracted, including first-order, second-order, and wavelet features. Reproducibility was assessed using Lin’s concordance correlation coefficient (CCC) on internal and external testing. We considered a radiomic feature with CCC $\ge0.85$ as a high-agreement feature. As a result, the average number of reproducible features increased in all protocols, from 241 ± 38 to 565 ± 11 in internal testing. In external testing, consisting of a new phantom and unseen protocol, 239 ± 74 reproducible features were in source images and 324 ± 16 were in generated images. The EHFS network is a novel approach to improving the reproducibility of radiomic features. It outperforms existing methods in reproducibility and generalization, as demonstrated by comprehensive experiments on both internal and external datasets. Our deep-learning-based CT image conversion could be a solution for standardization in ongoing radiomics research