RESUMEN

Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis. However, due to the complex morphology of atherosclerosis plaques and the lack of well-annotated data, the segmentation of lumen and wall is very challenging. Different from popular deep learning methods, in this paper, we propose an integration segmentation framework by introducing a lightweight prediction model and improved optimal surface graph cuts (OSG), which adopts a simplified flow line sampling and post-reconstructing method to reduce the cost of graph construction. Moreover, a flexibly adaptive smoothing penalty is presented for maintaining the shape of diseased carotid surface. For the experiments, we have collected an MR image dataset from patients with carotid atherosclerosis and evaluated our method by cross-validation. It can reach 89.68%/80.29% of dice coefficients and 0.2480 mm/0.3396 mm of average surface distances on the lumen/wall segmentation, respectively. The experimental results show that our method can generate precise and reliable segmentation of both lumen and wall of diseased carotid artery with a quite small training cost.

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RESUMEN

Cardiovascular disease (CVD) is a common disease with high mortality rate, and carotid atherosclerosis (CAS) is one of the leading causes of cardiovascular disease. Multisequence carotid MRI can not only identify carotid atherosclerotic plaque constituents with high sensitivity and specificity, but also obtain different morphological features, which can effectively help doctors improve the accuracy of diagnosis. However, it is difficult to evaluate the accurate evolution of local changes in carotid atherosclerosis in multi-sequence MRI due to the inconsistent parameters of different sequence images and the geometric space mismatch caused by the motion deviation of tissues and organs. To solve these problems, we propose a cross-scale multi-modal image registration method based on the Siamese U-Net. The network uses sub-networks with image inputs of different sizes to extract various features, and a special padding module is designed to make the network available for training on cross-scale features. In addition, to improve the registration performance, a multi-scale loss function under Gaussian smoothing is applied for optimization. For the experiments, we have collected a multi-sequence MRI image dataset from 11 patients with carotid atherosclerosis for a retrospective study. We evaluate our overall architectures by cross-validation on our carotid dataset. The experimental results show that our method can generate precise and reliable results with cross-scale multi-sequence inputs and the registration accuracy can be greatly improved by using the Gaussian smoothing loss function. The DSC of our Siamese structure can reach 84.1% on the carotid data set with cross-size input. With the use of GDSC loss, the average DSC can be improved by 5.23%, while the average distance between fixed landmarks and moving landmarks can be decreased by 6.46%.Our code is made publicly available at: https://github.com/MingHan98/Cross-scale-Siamese-Unet.

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The motion behaviors of a rigid body can be characterized by a six degrees of freedom motion trajectory, which contains the 3-D position vectors of a reference point on the rigid body and 3-D rotations of this rigid body over time. This paper devises a rotation and relative velocity (RRV) descriptor by exploring the local translational and rotational invariants of rigid body motion trajectories, which is insensitive to noise, invariant to rigid transformation and scale. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies. To show the descriptive ability of our RRV descriptor, we explore its potentials and applications in different rigid body motion recognition tasks. The experimental results on benchmark datasets demonstrate that our RRV descriptor learning discriminative motion patterns can achieve superior results for various recognition tasks.

RESUMEN

Motion trajectories tracked from the motions of human, robots, and moving objects can provide an important clue for motion analysis, classification, and recognition. This paper defines some new integral invariants for a 3-D motion trajectory. Based on two typical kernel functions, we design two integral invariants, the distance and area integral invariants. The area integral invariants are estimated based on the blurred segment of noisy discrete curve to avoid the computation of high-order derivatives. Such integral invariants for a motion trajectory enjoy some desirable properties, such as computational locality, uniqueness of representation, and noise insensitivity. Moreover, our formulation allows the analysis of motion trajectories at a range of scales by varying the scale of kernel function. The features of motion trajectories can thus be perceived at multiscale levels in a coarse-to-fine manner. Finally, we define a distance function to measure the trajectory similarity to find similar trajectories. Through the experiments, we examine the robustness and effectiveness of the proposed integral invariants and find that they can capture the motion cues in trajectory matching and sign recognition satisfactorily.

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