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Texture plays an important role in cartoon images to represent materials of objects and enrich visual attractiveness. However, manually crafting a cartoon texture is not easy, so amateurs usually directly use cartoon textures downloaded from the Internet. Unfortunately, Internet resources are quite limited and often patented, which restrict the users from generating visually pleasant and personalized cartoon textures. In this paper, we propose a deep learning based method to generate cartoon textures from natural textures. Different from the existing photo cartoonization methods that only aim to generate cartoonic images, the key to our method is to generate cartoon textures that are both cartoonic and regular. To achieve this goal, we propose a regularization module to generate a regular natural texture with similar appearance as the input, and a cartoonization module to cartoffonize the regularized natural texture into a regular cartoon texture. Our method successfully produces cartoonic and regular textures from various natural textures.

Skin lesion segmentation from dermoscopic image is essential for improving the quantitative analysis of melanoma. However, it is still a challenging task due to the large scale variations and irregular shapes of the skin lesions. In addition, the blurred lesion boundaries between the skin lesions and the surrounding tissues may also increase the probability of incorrect segmentation. Due to the inherent limitations of traditional convolutional neural networks (CNNs) in capturing global context information, traditional CNN-based methods usually cannot achieve a satisfactory segmentation performance. In this paper, we propose a novel feature adaptive transformer network based on the classical encoder-decoder architecture, named FAT-Net, which integrates an extra transformer branch to effectively capture long-range dependencies and global context information. Furthermore, we also employ a memory-efficient decoder and a feature adaptation module to enhance the feature fusion between the adjacent-level features by activating the effective channels and restraining the irrelevant background noise. We have performed extensive experiments to verify the effectiveness of our proposed method on four public skin lesion segmentation datasets, including the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets. Ablation studies demonstrate the effectiveness of our feature adaptive transformers and memory-efficient strategies. Comparisons with state-of-the-art methods also verify the superiority of our proposed FAT-Net in terms of both accuracy and inference speed. The code is available at https://github.com/SZUcsh/FAT-Net.

Mesh optimization has been studied from the graphical point of view: It often focuses on 3D surfaces obtained by optical and laser scanners. This is despite the fact that isosurfaced meshes of medical image reconstruction suffer from both staircases and noise: Isotropic filters lead to shape distortion, while anisotropic ones maintain pseudo-features. We present a data-driven method for automatically removing these medical artifacts while not introducing additional ones. We consider mesh optimization as a combination of vertex filtering and facet filtering in two stages: Offline training and runtime optimization. In specific, we first detect staircases based on the scanning direction of CT/MRI scanners, and design a staircase-sensitive Laplacian filter (vertex-based) to remove them; and then design a unilateral filtered facet normal descriptor (uFND) for measuring the geometry features around each facet of a given mesh, and learn the regression functions from a set of medical meshes and their high-resolution reference counterparts for mapping the uFNDs to the facet normals of the reference meshes (facet-based). At runtime, we first perform staircase-sensitive Laplacian filter on an input MC (Marching Cubes) mesh, and then filter the mesh facet normal field using the learned regression functions, and finally deform it to match the new normal field for obtaining a compact approximation of the high-resolution reference model. Tests show that our algorithm achieves higher quality results than previous approaches regarding surface smoothness and surface accuracy.

With the rapid development of deep learning, image semantic segmentation has made great progress and become a hot topic in scene understanding of computer vision. In this paper, we propose an optimized high-resolution net (HRNet) for image semantic segmentation. Unlike traditional networks usually extract feature maps based on a high-to-low encoder, which may easily loss important shape and boundary details especially for the deeper layers with lower resolutions, our optimized HRNet can maintain high resolution features at all times using a relatively shallow and parallel network structure. To improve the ability of our model in better recognizing the objects with various scales and irregular shapes, we introduce a mixed dilated convolution (MDC) module, which can not only increase the diversity of the receptive fields, but also tackle the “gridding” problem commonly existing in the conventional dilated convolution. By minimizing fine detail lost based on a DUpsample strategy, we further develop a multi-level data-dependent feature aggregation (MDFA) module to enhance the capability of our network in better identifying the fine details especially for the small objects with fuzzy boundaries. We evaluate the optimized HRNet on four different datasets, including Cityscapes, Pascal VOC2012, CamVid and the KITTI. Experimental results validate the effectiveness of our method in improving the accuracy of image semantic segmentation. Comparisons with state-of-the-art methods also verify the advantages of our optimized HRNet in achieving better semantic segmentation performance.

Accurately segmenting retinal vessel from retinal images is essential for the detection and diagnosis of many eye diseases. However, it remains a challenging task due to (1) the large variations of scale in the retinal vessels and (2) the complicated anatomical context of retinal vessels, including complex vasculature and morphology, the low contrast between some vessels and the background, and the existence of exudates and hemorrhage. It is difficult for a model to capture representative and distinguishing features for retinal vessels under such large scale and semantics variations. Limited training data also make this task even harder. In order to comprehensively tackle these challenges, we propose a novel scale and context sensitive network (a.k.a., SCS−Net) for retinal vessel segmentation. We first propose a scale-aware feature aggregation (SFA) module, aiming at dynamically adjusting the receptive fields to effectively extract multi-scale features. Then, an adaptive feature fusion (AFF) module is designed to guide efficient fusion between adjacent hierarchical features to capture more semantic information. Finally, a multi-level semantic supervision (MSS) module is employed to learn more distinctive semantic representation for refining the vessel maps. We conduct extensive experiments on the six mainstream retinal image databases (DRIVE, CHASEDB1, STARE, IOSTAR, HRF, and LES-AV). The experimental results demonstrate the effectiveness of the proposed SCS-Net, which is capable of achieving better segmentation performance than other state-of-the-art approaches, especially for the challenging cases with large scale variations and complex context environments.

CaCu3Ti4O12 (CCTO) nano powder was synthesized using a molten salt synthesis method in NaCl flux. Synthesis temperature and holding time were investigated. The suitable synthesis condition is 800 °C for 2 h. Aqueous CCTO slurry with high solid loading and low viscosity was prepared by using acrylic acid-2-acrylamido-2-methypropane sulfonic acid copolymer (AA/AMPS) as the dispersant. AA/AMPS dosage and pH condition have been optimized as AA/AMPS dosage of 3 wt% and pH about 9.08. A low-toxicity and water-soluble monomer, N,N-dimethylacrylamide (DMAA) was used as the gelling agent. CCTO green body fabricated by the gelcasting method has the homogeneous microstructure and relatively high mechanical strength of 9.27 MPa. CCTO ceramics obtained by the gelcasting method have higher dielectric constant than those prepared by the cold isostatic pressing method and show relatively low dielectric loss of below 0.2 in the wide frequency range of 102–105 Hz.

B-spline based deformable model is commonly used in recovering three-dimensional (3D) cardiac motion from tagged MRI due to its compact description, localized continuity and control flexibility. However, existing approaches usually ignore an important well-known fact that myocardial tissue is incompressible. In this paper, we propose to reconstruct 3D cardiac motion from tagged MRI using an incompressible B-solid model. We demonstrate that cardiac motion recovery can be achieved more with greater accuracy by considering both smoothness and incompressibility of the myocardium. Specifically, our incompressible B-solid model is formulated as a 3D tensor product of B-splines, where each piece of B-spline represents a smooth and divergence-free displacement field of myocardium with respect to radial, longitudinal and circumferential direction, respectively. We further formulate the fitting of the incompressible B-solid model as an optimization problem and solve it with a two-stage algorithm. Finally, the 3D myocardium strains are obtained from the reconstructed incompressible displacement fields and visualized in a comprehensive way. The proposed method is evaluated on both synthetic and in vivo human datasets. Comparisons with state-of-the-art methods are also conducted to validate the proposed method. Experimental results demonstrate that our method has a higher accuracy and more stable volume-preserving ability than previous methods, yielding an average displacement error of 0.21 mm and a Jacobian determinant mean of 1.029.

level set formulation and partial differential equations. The weighted endocardium surface and epicardium surface are then varying according to the local echo sampling density. To handle the noisy and non-uniform regions within the echo data, we used a regularization term to adaptively and continuously following the gradient descent of the topological change. Finally, the optimal the endocardium surface and epicardium surface are obtained by solving the minimal energy for the two weighted surfaces using a brief propagation algorithm. Results: Even for the typically low signal-to-noise and small field-ofview in echo images, our method can successfully segment the left ventricle by robustly extracting the endocardium surface and epicardium surface. The proposed method achieves a mean accuracy of 6.1±1.3% volume differences against the gold standard for MRI segmentation method and a mean accuracy of 14.5±2.1% for segmentation of all four chambers. Conclusion: Using a level set approach to segment left ventricle in 3D echocardiography provides a fully automatic tool for physicians to obtain the anatomical and diagnostic information for cardiac functional analysis, without requiring any user input or any other assistants. The ability of our method to automatically and accurately extract endocardium and epicardium from echo has promising clinical potential in further medical diagnosis and surgery planning.