Alveolar soft part sarcoma (ASPS) is a malignancy with low incidence, but with poor prognosis if misdiagnosed. Immunohistochemical assay using TFE3 antibody has been shown to be a sensitive technique for ASPS diagnosis. A specific chromosomal translocation, t(X;17)(p11.2;q25), results in the ASPL-TFE3 fusion gene: it is detectable using reverse-transcription polymerase chain reaction (RT-PCR) in frozen tumor tissues of ASPS. However, the diagnostic usefulness of these markers has not been investigated in Chinese ASPS patients. Here, we report the first systematic study applying TFE3 immunoassay and ASPL-TFE3 fusion transcript detection to archival paraffin-embedded tissues in a large Chinese ASPS patient population. Sixteen patients had been diagnosed with ASPS (age, 3 to 58 y; 3 male patients and 13 female patients). Their tumors presented predominantly in the extremities (8/16), and were often located in the region of the orbit when affecting infants and children (3/16). Others had tumors in the chest wall, breast, and right pubis, respectively. One patient exhibited a tumor in the renal hilum, a location that had not been previously reported. Two patients had tumor metastases in the lung and the brain. ASPS tumors showed the best immunoreactivity to the TFE3 antibody (16/16). However, their immunoreactivity to other antibodies, including myoglobin (13/16), actin (10/16), desmin (2/16), and vimentin (2/16), was of various degrees. Positive staining was observed for the neural markers, NSE (9/16) and CgA (7/16), respectively. Using a strategy of RT-PCR, followed by a nested PCR with a different primer set, we were able to detect the expression of the chimeric ASPL-TFE3 mRNA in 11 of the 16 ASPS tumors. Of these 11, 7were type 1 ASPL-TFE3 and 4 were type 2 ASPL-TFE3, including the tumor located in the renal hilum. No expression of ASPL-TFE3 fusion transcripts was detectable in all 38 control tumors. Our results demonstrate that the ''bimarker strategy,'' a combination of TFE3 immunostaining and ASPL-TFE3 chimeric transcript detection, might have sufficient sensitivity and specificity in diagnosing most of the ASPSs. As both diagnostic techniques can be applied to widely available archival paraffinembedded tissues, the usefulness of the strategy is largely implicated in routine pathology laboratories.

Color medical images better reflect a patient's lesion information and facilitate communication between doctors and patients. The combination of medical image processing and the Internet has been widely used for clinical medicine on Internet of medical things. The classical Welsh method uses matching pixels to achieve color migration of grayscale images, but it exists problems such as unclear boundary and single coloring effect. Therefore, the key information of medical images after coloring can't be reflected efficiently. To address this issue, we propose an image coloring method based on Gabor filtering combined with Welsh coloring and apply it to medical grayscale images. By using Gabor filtering, which is similar to the visual stimulus response of simple cells in the human visual system, filtering in 4 directions and 6 scales is used to stratify the grayscale images and extract local spatial and frequency domain information. In addition, the Welsh coloring method is used to render the image with obvious textural features in the layered image. Our experiments show that the color transboundary problem can be solved effectively after the layered processing. Compared to images without stratification, the coloring results of the processed images are closer to the real image.

Visualization is one of the most intuitive and perceptible ways for information representation in the big data era. As an essential part of the visualization, 3D mesh reconstruction is facing great challenges due to its characteristics of quantity, non-structure, and low-accuracy. The traditional 3D mesh reconstruction method has strict theoretical proof and can be used to reconstruct the surface of the complex topological structure for computer rendering and display. However, it is not suitable to handle a large number of point cloud and noise point cloud in a big data platform because the process is inefficient, low-automation and requires massive calculations. To address this issue, we propose a region growing based 3D mesh reconstruction (R3MR) in the big data platform. Firstly, we divide the large data points into three categories: flat point set, high curvature point set, and boundary point set. The errors of topological structure for 3D meshes usually occur in the place with large curvatures and noise points, so the division of high curvature point set is beneficial to solve the low-accuracy problem in 3D mesh reconstruction. Moreover, the flat points can be treated as one kind of point to avoid repetitive calculations because their features are basically the same. Hence, the division of the flat point set is beneficial to solve the problem of quantity and massive calculations. Secondly, our proposal is to start the mesh reconstruction from the flat point set progressively, because it can obtain the outline of the 3D model. In many scenarios, such as autonomous driving, only the overall outline of the model is required. Finally, during the 3D mesh reconstruction, the inner edge adjacency list and optimal selection principle are set to improve the robustness of the whole system. Simulation experiments show that the proposed 3D mesh reconstruction can naturally reflect the detailed features of objects in the big data platform, especially effective for the scattered point cloud.

As an important means of medical imaging, elastic imaging is an indispensable part of mobile telemedicine. Ultrasound elastography has become a research hotspot because it can accurately measure soft tissue lesions. Displacement estimation is the most important step in ultrasound elastography. At present, the phase zero search method is an accurate and fast displacement estimation method. However, when the displacement exceeds 1/4 wavelength, it is invalid. The accuracy of block matching method is not high, but it is suitable for large displacement, so it can overcome this shortcoming. It is worth noting that the quality-guided block matching method has good robustness under complex mutation conditions. It can provide prior knowledge to increase the robustness of the phase-zero search under large displacement conditions. So we propose a novel displacement estimation method for real time tissue ultrasound elastography, which combines the quality-guided block matching method and the phase-zero search method. The experimental results show that this method is more accurate, faster and robust than other displacement estimation methods.

Image style transfer can realize the mutual transfer between different styles of images and is an essential application for big data systems. The use of neural network-based image data mining technology can effectively mine the useful information in the image and improve the utilization rate of information. However, when using the deep learning method to transform the image style, the content information is often lost. To address this problem, this paper introduces L1 loss on the basis of the VGG-19 network to reduce the difference between image style and content and adds perceptual loss to calculate the semantic information of the feature map to improve the model’s perceptual ability. Experiments show that the proposal in this paper improves the ability of style transfer, while maintaining image content information. The stylization of the improved model can better meet people’s requirements for stylization, and the evaluation indexes of structural similarity, cosine similarity, and mutual information value have increased by 0.323%, 0.094%, and 3.591%, respectively.