Micro-CT can play an important role in preclinical studies of cardiovascular disease because of its high spatial and temporal resolution. Quantitative analysis of 4D cardiac images requires segmentation of the cardiac chambers at each time point, an extremely time consuming process if done manually. To improve throughput this study proposes a pipeline for registration-based segmentation and functional analysis of 4D cardiac micro-CT data in the mouse. Following optimization and validation using simulations, the pipeline was applied to in vivo cardiac micro-CT data corresponding to 10 cardiac phases acquired in C57BL/6 mice (n = 5). After edge-preserving smoothing with a novel adaptation of 4D bilateral filtration, one phase within each cardiac sequence was manually segmented. Deformable registration was used to propagate these labels to all other cardiac phases for segmentation. The volumes of each cardiac chamber were calculated and used to derive stroke volume, ejection fraction, cardiac output, and cardiac index. Dice coefficients and volume accuracies were used to compare manual segmentations of two additional phases with their corresponding propagated labels. Both measures were, on average, >0.90 for the left ventricle and >0.80 for the myocardium, the right ventricle, and the right atrium, consistent with trends in inter- and intra-segmenter variability. Segmentation of the left atrium was less reliable. On average, the functional metrics of interest were underestimated by 6.76% or more due to systematic label propagation errors around atrioventricular valves; however, execution of the pipeline was 80% faster than performing analogous manual segmentation of each phase.
Total variation (TV) regularization is a technique commonly utilized to promote sparsity of image in gradient domain. In this article, we address the problem of MR brain image reconstruction from highly undersampled Fourier measurements. We define the Moreau enhanced function of L 1 norm, and introduce the minmax-concave TV (MCTV) penalty as a regularization term for MR brain image reconstruction. MCTV strongly induces the sparsity in gradient domain, and fits the frame of fast algorithms (eg, ADMM) for solving optimization problems. Although MCTV is non-convex, the cost function in each iteration step can maintain convexity by specifying the relative nonconvexity parameter properly. Experimental results demonstrate the superior performance of the proposed method in comparison with standard TV as well as nonlocal TV minimization method, which suggests that MCTV may have promising applications in the field of neuroscience in the future. K E Y W O R D S brain image reconstruction, magnetic resonance imaging, non-convex regularization, total variation Int J Imaging Syst Technol. 2018;1-8. wileyonlinelibrary.com/journal/ima V C 2017 Wiley Periodicals, Inc. | 1
Video face detection technology has a wide range of applications, such as video surveillance, image retrieval, and human-computer interaction. However, face detection always has some uncontrollable interference factors in the video sequence, such as changes in lighting, complex backgrounds, and face changes in scale and occlusion conditions, etc. Therefore, this paper introduces deep learning theory and combines the continuity characteristics of video sequences to make related research on video face detection algorithms based on deep learning. First, this algorithm uses the residual network as the basic network of the Single Shot MultiBox Detector (SSD) target detection network model and trains a Rest-SSD face detection model to detect faces. Experimental results show that the method can achieve real-time detection and improve the accuracy of video face detection, which is required for face detection in a video. Then we based on the continuity characteristics of video sequences. This paper proposes a video face detection method based on the training of the Rest-SSD face detection model. The method first uses kernel correlation filtering to track consecutive n frames according to the detection results, sets weights on the confidence of the n frames of tracking results, uses the weighted average method to calculate the best tracking result, and then sets the best tracking result confidence and the current frame sets the appropriate weights for the confidence of the detection result for fusion, thereby improving the video face detection accuracy.
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