DLFFNet: A new dynamical local feature fusion network for automatic aortic valve calcification recognition using echocardiography

CMR-BENet: A confidence map refinement boundary enhancement network for left ventricular myocardium segmentation

BACKGROUND AND OBJECTIVE

Left ventricular myocardium segmentation is of great significance for clinical diagnosis, treatment, and prognosis. However, myocardium segmentation is challenging as the medical image quality is disturbed by various factors such as motion, artifacts, and noise. Its accuracy largely depends on the accurate identification of edges and structures. Most existing encoder-decoder based segmentation methods capture limited contextual information and ignore the awareness of myocardial shape and structure, often producing unsatisfactory boundary segmentation results in noisy scenes. Moreover, these methods fail to assess the reliability of the predictions, which is crucial for clinical decisions and applications in medical tasks. Therefore, this study explores how to effectively combine contextual information with myocardial edge structure and confidence maps to improve segmentation performance in an end-to-end network.

METHODS

In this paper, we propose an end-to-end confidence map refinement boundary enhancement network (CMR-BENet) for left ventricular myocardium segmentation. CMR-BENet has three components: a layer semantic-aware module (LSA), an edge information enhancement module (EIE), and a confidence map-based refinement module (CMR). Specifically, LSA first adaptively fuses high- and low-level semantic information across hierarchical layers to mitigate the bias of single-layer features affected by noise. EIE then improves the edge and structure recognition by designing the edge and mask guidance module (EMG) and the edge structure-aware module (ESA). Finally, CMR provides a simple and efficient way to estimate confidence maps and effectively combines the encoder features to refine the segmentation results.

RESULTS

Experiments on two echocardiography datasets and one cardiac MRI dataset show that the proposed CMR-BENet outperforms its rivals in the left ventricular myocardium segmentation task with Dice (DI) of 87.71%, 79.33%, and 89.11%, respectively.

CONCLUSION

This paper utilizes edge information to characterize the shape and structure of the myocardium and introduces learnable confidence maps to evaluate and refine the segmentation results. Our findings provide strong support and reference for physicians in diagnosis and treatment.

Aortic Valve Calcium Score: Applications in Clinical Practice and Scientific Research-A Narrative Review

In this narrative review, we investigate the essential role played by the computed tomography Aortic Valve Calcium Score (AVCS) in the cardiovascular diagnostic landscape, with a special focus on its implications for clinical practice and scientific research. Calcific aortic valve stenosis is the most prevalent type of aortic stenosis (AS) in industrialized countries, and due to the aging population, its prevalence is increasing. While transthoracic echocardiography (TTE) remains the gold standard, AVCS stands out as an essential complementary tool in evaluating patients with AS. The advantage of AVCS is its independence from flow; this allows for a more precise evaluation of patients with discordant findings in TTE. Further clinical applications of AVCS include in the assessment of patients before transcatheter aortic valve replacement (TAVR), as it helps in predicting outcomes and provides prognostic information post-TAVR. Additionally, we describe different AVCS thresholds regarding gender and the anatomical variations of the aortic valve. Finally, we discuss various scientific studies where AVCS was applied. As AVCS has some limitations, due to the pathophysiologies of AS extending beyond calcification and gender differences, scientists strive to validate contrast-enhanced AVCS. Furthermore, research on developing radiation-free methods of measuring calcium content is ongoing.

Mechanisms of Cardiovascular Calcification and Experimental Models: Impact of Vitamin K Antagonists

Cardiovascular calcification is a multifactorial and complex process involving an array of molecular mechanisms eventually leading to calcium deposition within the arterial walls. This process increases arterial stiffness, decreases elasticity, influences shear stress events and is related to an increased risk of morbidity and mortality associated with cardiovascular disease. In numerous in vivo and in vitro models, warfarin therapy has been shown to cause vascular calcification in the arterial wall. However, the exact mechanisms of calcification formation with warfarin remain largely unknown, although several molecular pathways have been identified. Circulating miRNA have been evaluated as biomarkers for a wide range of cardiovascular diseases, but their exact role in cardiovascular calcification is limited. This review aims to describe the current state-of-the-art research on the impact of warfarin treatment on the development of vascular calcification and to highlight potential molecular targets, including microRNA, within the implicated pathways.