Spatio-Temporal Segmentation in 3-D Echocardiographic Sequences Using Fractional Brownian Motion

BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D Echocardiography

Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently been proposed for the automatic segmentation of the LV, showing significant performance improvement over the traditional segmentation algorithms. However, unlike the traditional methods, prior information about the segmentation problem, e.g., anatomical shape information, is not usually incorporated for training the DL algorithms. This can degrade the generalization performance of the DL models on unseen images if their characteristics are somewhat different from those of the training images, e.g., low-quality testing images. In this study, a new shape-constrained deep convolutional neural network (CNN)-called B-spline explicit active surface (BEAS)-Net-is introduced for automatic LV segmentation. The BEAS-Net learns how to associate the image features, encoded by its convolutional layers, with anatomical shape-prior information derived by the BEAS algorithm to generate physiologically meaningful segmentation contours when dealing with artifactual or low-quality images. The performance of the proposed network was evaluated using three different in vivo datasets and was compared with a deep segmentation algorithm based on the U-Net model. Both the networks yielded comparable results when tested on images of acceptable quality, but the BEAS-Net outperformed the benchmark DL model on artifactual and low-quality images.

Dynamic-Guided Spatiotemporal Attention for Echocardiography Video Segmentation

Left ventricle (LV) endocardium segmentation in echocardiography video has received much attention as an important step in quantifying LV ejection fraction. Most existing methods are dedicated to exploiting temporal information on top of 2D convolutional networks. In addition to single appearance semantic learning, some research attempted to introduce motion cues through the optical flow estimation (OFE) task to enhance temporal consistency modeling. However, OFE in these methods is tightly coupled to LV endocardium segmentation, resulting in noisy inter-frame flow prediction, and post-optimization based on these flows accumulates errors. To address these drawbacks, we propose dynamic-guided spatiotemporal attention (DSA) for semi-supervised echocardiography video segmentation. We first fine-tune the off-the-shelf OFE network RAFT on echocardiography data to provide dynamic information. Taking inter-frame flows as additional input, we use a dual-encoder structure to extract motion and appearance features separately. Based on the connection between dynamic continuity and semantic consistency, we propose a bilateral feature calibration module to enhance both features. For temporal consistency modeling, the DSA is proposed to aggregate neighboring frame context using deformable attention that is realized by offsets grid attention. Dynamic information is introduced into DSA through a bilateral offset estimation module to effectively combine with appearance semantics and predict attention offsets, thereby guiding semantic-based spatiotemporal attention. We evaluated our method on two popular echocardiography datasets, CAMUS and EchoNet-Dynamic, and achieved state-of-the-art.