Automated mitral inflow Doppler peak velocity measurement using deep learning

Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application

Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI)

Abstract Non-invasive glioma grade classification is an exciting area in neuroimaging. The primary purpose of this study is to investigate the performance of different medical image fusion algorithms for glioma grading purposes by fusing advanced Magnetic Resonance Imaging (MRI) images. Ninety-six subjects underwent an Apparent diffusion coefficient (ADC) map and Susceptibility-weighted imaging (SWI) MRI scan. After preprocessing, the different medical image fusion methods used to fuse ADC maps and SWI were Principal Component Analysis (PCA), Structure-Aware, Discrete Cosine Harmonic Wavelet Transform (DCHWT), Deep-Convolutional Neural network (DNN), Dual-Discriminator conditional generative adversarial network (DDcGAN), and Laplacian Re-Decomposition (LRD). The Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and Relative Signal Contrast (RSC) were calculated for qualitative and quantitative analysis. We found high fused image quality with LRD and DDcGAN methods. Further quantitative analysis showed that RSCs in fused images in Low-Grade glioma (LGG) were significantly higher than RSCs in High-Grade glioma (HGG) with PCA, DCHWT, LRD, and DDcGAN. The Receiver Operating Characteristic (ROC) curve test highlighted that LRD and DDcGAN have the highest performance for glioma grade classification. Our work suggests using the DDcGAN and LRD networks for glioma grade classification by fusing ADC maps and SWI images

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning

COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R2. The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R2 are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model

Active Learning for Left Ventricle Segmentation in Echocardiography

Background and Objective: Training deep learning models for medical image segmentation requires large annotated datasets, which can be expensive and time-consuming to create. Active learning is a promising approach to reduce this burden by strategically selecting the most informative samples for segmentation. This study investigates the use of active learning for efficient left ventricle segmentation in echocardiography with sparse expert annotations. Methods: We adapt and evaluate various sampling techniques, demonstrating their effectiveness in judiciously selecting samples for segmentation. Additionally, we introduce a novel strategy, Optimised Representativeness Sampling, which combines feature-based outliers with the most representative samples to enhance annotation efficiency. Results: Our findings demonstrate a substantial reduction in annotation costs, achieving a remarkable 99% upper bound performance while utilizing only 20% of the labelled data. This equates to a reduction of 1680 images needing annotation within our dataset. When applied to a publicly available dataset, our approach yielded a remarkable 70% reduction in required annotation efforts, representing a significant advancement compared to baseline active learning strategies, which achieved only a 50% reduction. Our experiments highlight the nuanced performance of diverse sampling strategies across datasets within the same domain. Conclusions: The study provides a cost-effective approach to tackle the challenges of limited expert annotations in echocardiography. By introducing a distinct dataset, made publicly available for research purposes, our work contributes to the field’s understanding of efficient annotation strategies in medical image segmentation

Deep Active Learning for Left Ventricle Segmentation in Echocardiography

The training of advanced deep learning algorithms for medical image interpretation requires precisely annotated datasets, which is laborious and expensive. Therefore, this research investigates state-of-the-art active learning methods for utilising limited annotations when performing automated left ventricle segmentation in echocardiography. Our experiments reveal that the performance of different sampling strategies varies between datasets from the same domain. Further, an optimised method for representativeness sampling is introduced, combining images from feature-based outliers to the most representative samples for label acquisition. The proposed method significantly outperforms the current literature and demonstrates convergence with minimal annotations. We demonstrate that careful selection of images can reduce the number of images needed to be annotated by up to 70%. This research can therefore present a cost-effective approach to handling datasets with limited expert annotations in echocardiography

Automated Analysis of Mitral Inflow Doppler Using Deep Neural Networks

Doppler echocardiography is a widely applied modality for the functional assessment of heart valves, such as the mitral valve. Currently, Doppler echocardiography analysis is manually performed by human experts. This process is not only expensive and time-consuming, but often suffers from intra- and inter-observer variability. An automated analysis tool for non-invasive evaluation of cardiac hemodynamic has potential to improve accuracy, patient outcomes, and save valuable resources for health services. Here, a robust algorithm is presented for automatic Doppler Mitral Inflow peak velocity detection utilising state-of-the-art deep learning techniques. The proposed framework consists of a multi-stage convolutional neural network which can process Doppler images spanning arbitrary number of heartbeats, independent from the electrocardiogram signal and any human intervention. Automated measurements are compared to Ground-truth annotations obtained manually by human experts. Results show the proposed model can efficiently detect peak mitral inflow velocity achieving an average F1 score of 0.88 for both E- and A-peaks across the entire test set