A novel method combining a faster region-based convolutional neural network and active shape model accurately segmented and tracked the left ventricle, outperforming four state-of-the-art approaches.
Does the proposed method improve left ventricle recognition, segmentation, and tracking in cardiac ultrasound image sequences compared to conventional methods?
A novel deep learning and active shape model-based approach improves the automated recognition, segmentation, and tracking of the left ventricle in noisy cardiac ultrasound images.
In this study, we propose a novel method incorporating faster region-based convolutional neural network and active shape model to automatically recognize, segment, and track the left ventricle in cardiac ultrasound image sequences, respectively. Ultrasound images typically contain noise and artifacts. The conventional filters cannot preserve the edges of image contours, and thus blurry images are often obtained. In this study, we propose an improved adaptive anisotropic diffusion filter to effectively reduce noise and reinforce image contours. In addition, because of the shape and appearance of the left ventricle vary considerably between adjacent images, conventional methods cannot automatically identify the position of the left ventricle or accurately segment them. A novel method that combines the faster region-based convolutional neural network with the active shape model is proposed to automatically recognize, segment, and track the left ventricle in cardiac ultrasound image sequences. Compared with four state-of-the-art approaches, the method proposed in this study can be applied to accurately segment and track the left ventricle in cardiac ultrasound image sequences. The proposed method produces the most satisfactory results in terms of visual presentation and segmentation quality based on four criteria.
Wei‐Yen Hsu (Tue,) conducted a other in Left ventricle segmentation in cardiac ultrasound. Faster region-based convolutional neural network and active shape model vs. Four state-of-the-art approaches was evaluated on Visual presentation and segmentation quality based on four criteria. A novel method combining a faster region-based convolutional neural network and active shape model accurately segmented and tracked the left ventricle, outperforming four state-of-the-art approaches.