STGA-MS: AI diagnosis model of regional wall motion abnormality based on 2D transthoracic echocardiography.

Regional wall motion abnormality (RWMA) is a common manifestation of ischemic heart disease detected through echocardiography. Currently, RWMA diagnosis heavily relies on visual assessment by doctors, leading to limitations in experience-based dependence and suboptimal reproducibility among observers. Several RWMA diagnosis models were proposed, while RWMA diagnosis with more refined segments can provide more comprehensive wall motion information to better assist doctors in the diagnosis of ischemic heart disease. In this paper, we proposed the STGA-MS model which consists of three modules, the spatial-temporal grouping attention (STGA) module, the segment feature extraction module, and the multiscale downsampling module, for the diagnosis of RWMA for multiple myocardial segments. The STGA module captures global spatial and temporal information, enhancing the representation of myocardial motion characteristics. The segment feature extraction module focuses on specific segment regions, extracting relevant features. The multiscale downsampling module analyzes myocardial motion deformation across different receptive fields. Experimental results on a 2D transthoracic echocardiography dataset show that the proposed STGA-MS model achieves better performance compared to state-of-the-art models. It holds promise in improving the accuracy and reproducibility of RWMA diagnosis, assisting clinicians in diagnosing ischemic heart disease more reliably.

Learning what and where to segment: A new perspective on medical image few-shot segmentation.

Traditional medical image segmentation methods based on deep learning require experts to provide extensive manual delineations for model training. Few-shot learning aims to reduce the dependence on the scale of training data but usually shows poor generalizability to the new target. The trained model tends to favor the training classes rather than being absolutely class-agnostic. In this work, we propose a novel two-branch segmentation network based on unique medical prior knowledge to alleviate the above problem. Specifically, we explicitly introduce a spatial branch to provide the spatial information of the target. In addition, we build a segmentation branch based on the classical encoder-decoder structure in supervised learning and integrate prototype similarity and spatial information as prior knowledge. To achieve effective information integration, we propose an attention-based fusion module (AF) that enables the content interaction of decoder features and prior knowledge. Experiments on an echocardiography dataset and an abdominal MRI dataset show that the proposed model achieves substantial improvements over state-of-the-art methods. Moreover, some results are comparable to those of the fully supervised model. The source code is available at github.com/warmestwind/RAPNet.

EchoEFNet: Multi-task deep learning network for automatic calculation of left ventricular ejection fraction in 2D echocardiography.

Left ventricular ejection fraction (LVEF) is essential for evaluating left ventricular systolic function. However, its clinical calculation requires the physician to interactively segment the left ventricle and obtain the mitral annulus and apical landmarks. This process is poorly reproducible and error prone. In this study, we propose a multi-task deep learning network EchoEFNet. The network use ResNet50 with dilated convolution as the backbone to extract high-dimensional features while maintaining spatial features. The branching network used our designed multi-scale feature fusion decoder to segment the left ventricle and detect landmarks simultaneously. The LVEF was then calculated automatically and accurately using the biplane Simpson's method. The model was tested for performance on the public dataset CAMUS and private dataset CMUEcho. The experimental results showed that the geometrical metrics and percentage of correct keypoints of EchoEFNet outperformed other deep learning methods. The correlation between the predicted LVEF and true values on the CAMUS and CMUEcho datasets was 0.854 and 0.916, respectively.

Automatic identification of septal flash phenomenon in patients with complete left bundle branch block.

Complete left bundle branch block (cLBBB) is an electrical conduction disorder associated with cardiac disease. Septal flash (SF) involves septal leftward contraction during early systole followed by a lengthening motion toward the right ventricle and affects several patients with cLBBB. It has been revealed that cLBBB patients with SF may be at risk of cardiac function reduction and poor prognosis. Therefore, accurate identification of SF may play a vital role in counseling patients about their prognosis. Generally, Septal flash is identified by echocardiography using visual "eyeballing". However, this conventional method is subjective as it depends on operator experience. In this study, we build a linear attention cascaded net (LACNet) capable of processing echocardiography to identify SF automatically. The proposed method consists of a cascaded CNN-based encoder and an LSTM-based decoder, which extract spatial and temporal features simultaneously. A spatial transformer network (STN) module is employed to avoid image inconsistency and linear attention layers are implemented to reduce data complexity. Moreover, the left ventricle (LV) area-time curve calculated from segmentation results can be considered as a new independent disease predictor as SF phenomenon leads to transient left ventricle area enlargement. Therefore, we added the left ventricle area-time curve to LACNet to enrich input data diversity. The result shows the possibility of using echocardiography to diagnose cLBBB with SF automatically.