Medical Image Segmentation with Deep Convolutional Neural Networks

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Quality Enhancement of 3D Models Reconstructed By RGB-D Camera Systems

Low-cost RGB-D cameras like Microsoft\u27s Kinect capture RGB data for each vertex while reconstructing 3D models from objects with obvious drawbacks of poor mesh and texture qualities due to their hardware limitations. In this thesis we propose a combined method that enhances geometrically and chromatically 3D models reconstructed by RGB-D camera systems. Our approach utilizes Butterfly Subdivision and Surface Fitting techniques to generate smoother triangle surface meshes, where sharp features can be well preserved or minimized by different Surface Fitting algorithms. Additionally the global contrast of mesh textures is enhanced by using a modified Histogram Equalization algorithm, in which the new intensity of each vertex is obtained by applying cumulative distribution function and calculating the accumulated normalized histogram of the texture. A number of experimental results and comparisons demonstrate that our method efficiently and effectively improves the geometric and chromatic quality of 3D models reconstructed from RGB-D cameras

Medical Image Segmentation with Deep Learning

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Dynamic Response and Safety Control of Newly Poured Secondary Lining Concrete under Large Section Tunnel Blasting-A Case Study of Longnan Tunnel of Ganshen High-speed Railway

The age of newly poured concrete is short, the cementation between aggregates is weak. At this time, the vibration will affect its performance. The secondary lining concrete newly poured in the tunnel is close to the work face and is susceptible to blasting vibration during construction. In order to study the safety threshold of blasting vibration velocity of newly poured secondary lining concrete in tunnels, the finite element model is established in ANSYS with the large-section Longnan tunnel project as an example. The attenuation law of vibration velocity in three directions of secondary lining under blasting load was analyzed by combining field blasting monitoring with numerical simulation, and the reliability of numerical simulation was verified. Through the numerical simulation results, the vibration velocity and von mises stress distribution of the newly poured secondary lining concrete of the tunnel are analyzed; combined with the dynamic tensile strength theory of concrete, the safety threshold of vibration velocity of newly poured secondary lining concrete of tunnel based on numerical calculation is established; through the indoor vibration test, taking the compressive strength and acoustic velocity of concrete as the indexes, the safety threshold of blasting vibration velocity of newly poured secondary lining concrete of tunnel based on shaking table test is obtained. Combined with the results of numerical simulation and vibration test, the safety threshold of blasting vibration velocity of newly poured secondary lining concrete in large-section tunnels is obtained, and the standard in this field is improved

Effect of Liuweibuqi capsule, a Chinese patent medicine, on the JAK1/STAT3 pathway and MMP9/TIMP1 in a chronic obstructive pulmonary disease rat model

AbstractObjectiveTo observe effect of Liuweibuqi Capsule, a Traditional Chinese Medicine (TCM), on the janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway and matrix metalloproteinases (MMPs) in a chronic obstructive pulmonary disease (COPD) rat model with lung deficiency in terms of TCM's pattern differentiation.MethodsRats were randomly divided into a normal group, model group, Liuweibuqi group, Jinshuibao group, and spleen aminopeptidase group (n= 10). Aside from the normal group, all rats were exposed to smoke plus lipopolysaccharide tracheal instillation to establish the COPD model with lung deficiency. Models were established after 28 days and then the normal and model groups were given normal saline (0.09 g/kg), Liuweibuqi group was given Liuweibuqi capsule (0.35 g/kg), Jinshuibao group was given Jinshuibao capsules (0.495 g/kg), and the spleen group was given spleen aminopeptidase (0.33 mg/kg), once a day for 30 days. Changes in symptoms, signs, and lung histology were observed. Lung function was measured with a spirometer. Serum cytokines were detected using enzyme-linked immunosorbent assay, and changes in the JAK/STAT pathway, MMP-9, and MMPs inhibitor 1 (TIMP1) were detected by immunohistochemistry, RT-PCR, and western blotting, respectively.ResultsCompared with the normal group, lung tissue was damaged, and lung function was reduced in the model control group. Additionally, the levels of interleukin (IL)-1β, γ interferon (IFN-γ), and IL-6 were higher, while IL-4 and IL-10 were lower in the model control group than those in the normal group. The expressions of JAK1, STAT3, p-STAT3, and MMP-9 mRNA and protein in lung tissue were higher, and TIMP1 mRNA and protein was lower in the model group compared with the normal group. After treatment, compared with the model group, the expression of inflammatory cytokines was lower in each treatment group, and expressions of JAK/STAT pathway, MMPs were lower. Compared with the positive control groups, the Jinshuibao and spleen aminopeptidase groups, lung function was better, and JAK1, STAT3, and p-STAT3 protein were lower and TIMP1 was higher in the Liuweibuqi group.ConclusionLiuweibuqi capsules can improve the symptoms of COPD possibly by regulating the expression of the JAK1/STAT3 pathway and MMP9/TIMP1