The use of echocardiography ventricle segmentation can obtain ventricular volume parameters, and it is helpful to evaluate cardiac function. However, the ultrasound images have the characteristics of high noise and difficulty in segmentation, bringing huge workload to segment the object region manually. Meanwhile, the automatic segmentation technology cannot guarantee the segmentation accuracy. In order to solve this problem, a novel algorithm framework is proposed to segment the ventricle. Firstly, faster region-based convolutional neural network is used to locate the object to get the region of interest. Secondly, K-means is used to pre-segment the image; then a mean shift with adaptive bandwidth of kernel function is proposed to segment the region of interest. Finally, the region growing algorithm is used to get the object region. By this framework, ventricle is obtained automatically without manual localization. Experiments prove that this framework can segment the object accurately, and the algorithm of adaptive mean shift is more stable and accurate than the mean shift with fixed bandwidth on quantitative evaluation. These results show that the method in this paper is helpful for automatic segmentation of left ventricle in echocardiography.
ObjectiveTo address the inter-observer annotation variability in echocardiographic image segmentation caused by image boundary ambiguity and differences in experts’ clinical backgrounds. MethodsWe proposed an uncertainty-aware consensus segmentation framework based on a conditional diffusion model. Conditioned on the input image, the framework employed a probabilistic consensus strategy to dynamically integrate annotations from multiple experts, jointly modeling stable anatomical consensus and clinically plausible annotation diversity. To compensate for the lack of real multi-expert annotations in publicly available datasets (e.g., CAMUS), we constructed a synthetic multi-expert annotation system using morphological operations to emulate three representative clinical labeling styles including conservative, moderate, and aggressive, providing a reliable foundation for method validation. ResultsThe proposed model significantly outperformed state-of-the-art methods in both left ventricular endocardium (LVEndo) and left ventricular epicardium (LVEpi) segmentation tasks. For LVEndo, the Generalized Energy Distance (\begin{document}$ GED{_{5}}{_{0}} $\end{document}) at the end-diastolic (ED) phase reached 0.073 1,representing a 43.9% reduction compared to the D-Persona model. For LVEpi, the \begin{document}$ GED{_{5}}{_{0}} $\end{document} decreased by 42.0% and 39.0% at the ED and end-systolic phases, respectively, relative to D-Persona. Furthermore, the structural fidelity improved by 2.7-3.5 percentage points for LVEndo and 1.4-2.2 percentage points for LVEpi compared to single-expert models, indicating a superior ability to capture diverse expert preferences. Conclusion By jointly modeling population-level consensus and annotation diversity, this work enables a unified characterization of anatomical structures and their associated annotation uncertainty in cardiac ultrasound images, offering a novel approach toward robust and interpretable segmentation in clinical settings.