Image-Based Brachytherapy in Cervical Cancer: Review and Experiences in Faculty of Medicine, Chiang Mai University

Cervical cancer is the one of the most common cancer in female patients inThailand. Radiotherapy has the role for the treatment of cervical cancer by postoperative, radical and palliative treatments. For radical radiotherapy, the combination of external beam radiation therapy and brachytherapy will be used to increase the tumor dose to curative goal. With the new development of medical images (Computed tomography (CT), Magnetic Resonance Imaging (MRI) or Ultrasonography (US)), the treatment with brachytherapy will be developed from point-based to volume-based concepts. Many studies reported the benefit of image-based brachytherapy over conventional brachytherapy and clinical benefit of using image-based brachytherapy in the treatment of cervical cancer.

A review on image-based rendering

Image-based rendering is important both in the field of computer graphics and computer vision,and it is also widely used in virtual reality technology.For more than two decades,people have done a lot of work on the research of image-based rendering,and these methods can be divided into two categories according to whether the geometric information of the scene is utilized.According to this classification,we introduce some classical methods and representative methods proposed in recent years.We also compare and analyze the basic principles,advantages and disadvantages of different methods.Finally,some suggestions are given for research directions on image-based rendering techniques in the future.

Three Dimensional Microstructure and Image-Based Simulation of a Fly Ash/Al Syntactic Foam Using X-ray Micro-Computed Tomography

An aluminum matrix syntactic foam, incorporated with hollow-structured fly ash particles, was fabricated by pressure infiltration technique. X-ray micro-computed tomography was used to characterize its heterogeneous microstructure three dimensionally （3D）. The quantification of some microstructure features, such as content and size distribution of hollow fly ash particles, was acquired in 3D. The tomographic data were exploited as a rapid method to generate a microstructurally accurate and robust 3D meshed model. The thermal transport behavior has been modeled using a commercial finite-element code to conduct steady state analyses. Simulation of the thermal conductivity showed good correlation with experimental result.

Hybrid approach used for extended image-based wavefront sensor-less adaptive optics

The stochastic paralld gradient descent （SPGD） algorithm is widely used in wavefront sensor-less adaptive optics （WSAO） systems. However, the convergence is relatively slow. Modal-based algorithms usually provide much faster convergence than SPGD; however, the limited actuator stroke of the deformable mirror （DM） often prohibits the sensing of higher-order modes or renders a closed-loop correction inapplicable. Based on a comparative analysis of SPGD and the DM-modal-based algorithm, a hybrid approach involving both algorithms is proposed for extended image-based WSAO, and is demonstrated in this experiment. The hybrid approach can achieve similar correction results to pure SPGD, but with a dramatically decreased iteration number.

Image-based autonomous navigation of Hayabusa2 using artificial landmarks: The design and brief in-flight results of the first landing on asteroid Ryugu

Hayabusa2 is an asteroid sample return mission carried out by the Japan Aerospace Exploration Agency.The spacecraft was launched in 2014 and arrived at the target asteroid Ryugu on June 27,2018.During the 1.5-year proximity phase,several critical operations(including two landing/sampling operations)were successfully performed.They were based on autonomous image-based descent and landing techniques.This paper describes an imagebased autonomous navigation scheme of the Hayabusa2 mission using artificial landmarks named target markers(TMs).Its basic algorithm,and the in-flight results of the first touchdown and its rehearsal,are shown.

Calibration-free and model-independent method for high-DOF image-based visual servoing

This paper presents a novel method to improve the performance of high-DOF image base visual servoing （IBVS） with an uncalibrated camera. Firstly, analysis and comparison between point-based and moment-based features are carried out with respect to a 4-DOF positioning task. Then, an extended interaction matrix （IM） related to the digital image, and a Kalman filter （KF）-based estimation algorithm of the extended IM without calibration and IM model are proposed. Finally, the KF-based algorithm is extended to realize an approximation to decoupled control scheme. Experimental results conducted on an industrial robot show that our proposed methods can provide accurate estimation of IM, and achieve similar performance compared with traditional calibration-based method. Therefore, the proposed methods can be applied to any robot control system in variational environments, and can realize instant operation to planar object with complex and unknown shape at large displacement.

Robust image-based coordinated control for spacecraft formation flying

This paper addresses a coordinated control problem for Spacecraft Formation Flying(SFF). The distributed followers are required to track and synchronize with the leader spacecraft.By using the feature points in the two-dimensional image space, an integrated 6-degree-of-freedom dynamic model is formulated for spacecraft relative motion. Without sophisticated threedimensional reconstruction, image features are directly utilized for the controller design. The proposed image-based controller can drive the follower spacecraft in the desired configuration with respect to the leader when the real-time captured images match their reference counterparts. To improve the precision of the formation configuration, the proposed controller employs a coordinated term to reduce the relative distance errors between followers. The uncertainties in the system dynamics are handled by integrating the adaptive technique into the controller, which increases the robustness of the SFF system. The closed-loop system stability is analyzed using the Lyapunov method and algebraic graph theory. A numerical simulation for a given SFF scenario is performed to evaluate the performance of the controller.

Image-based numerical study of three-dimensional meso-structure effects on damage and failure of heterogeneous coal-rock under dynamic impact loads

This paper proposes a numerical three-dimensional(3D)mesoscopic approach based on the discrete element method combined with X-ray computed tomography(XCT)images to characterize the dynamic impact behavior of heterogeneous coal-rock(HCR).The dynamic impact loading in three directions was modelled to investigate the effects of the 3D meso-structure on the failure patterns and fracture mechanism,with different impact velocities.The XCT image-based discrete element model of HCR was calibrated through appropriate standard uniaxial compression tests.Numerical simulations were carried out to investigate how the breakage behaviors are affected by different loading directions with different impact velocities.The loading direction,input energy,and spatial distribution of the mineral phase had a remarkable influence on the failure patterns and load-carrying capacities.The shape of the gangue phase and the approximate location of the gangue interfaces are key parameters to consider when investigating the failure patterns and fracture mechanism of heterogeneous rock materials.The damage and fracture tended to propagate from the surfaces to the HCR interior.The gangue phase area contacting the loading wall,growth direction of the strong gangue interfaces,and loading directions greatly influenced the failure patterns of the heterogeneous rock materials.

Layered Textures for Image-Based Rendering

An extension to texture mapping is given in this paper for improving theefficiency of image-based rendering. For a depth image with an orthogonal displacement at eachpixel, it is decomposed by the displacement into a series of layered textures (LTs) with each onehaving the same displacement for all its texels. Meanwhile, some texels of the layered textures areinterpolated for obtaining a continuous 3D approximation of the model represented in the depthimage. Thus, the plane-to-plane texture mapping can be used to map these layered textures to producenovel views and the advantages can be obtained as follows: accelerating the rendering speed,supporting the 3D surface details and view motion parallax, and avoiding the expensive task ofhole-filling in the rendering stage. Experimental results show the new method can producehigh-quality images and run faster than many famous image-based rendering techniques.

Multiview Visibility Estimation for Image-Based Modeling

In this paper, we investigate the problem of determining regions in 3D scene visible to some given viewpoints when obstacles are present in the scene. We assume that the obstacles are composed of some opaque objects with closed surfaces. The problem is formulated in an implicit framework where the obstacles are represented by a level set function. The visible and invisible regions of the given viewpoints are determined through an efficient implicit ray tracing technique. As an extension of our approach, we apply the multiview visibility estimation to an image-based modeling technique. The unknown scene geometry and multiview visibility information are incorporated into a variational energy functional. By minimizing the energy functional, the true scene geometry as well as the accurate visibility information of the multiple views can be recovered from a number of scene images. This makes it feasible to handle the visibility problem of multiple views by our approach when the true scene geometry is unknown.

A sparse representation method for image-based surface defect detection

In this paper, an efficient sparse representation-based method is presented for detecting surface defects. The proposed method uses the sparse degree of coefficient in the redundant dictionary for checking whether the test image is defective or not, and the binary representation of the defective images is obtained, according to the global coefficient feature. Owing to the requirements for the efficiency and detecting quality, the block proximal gradient operator is introduced to speed up the online dictionary learning. Considering the correlation among the testing samples, prior knowledge is applied in the orthogonal-matching-pursuit sparse representation algorithm to improve the speed of sparse coding. Experimental results demonstrate that the proposed detection method can effectively detect and extract the defects of the surface images, and has broad applicability.

Lumiproxy:A Hybrid Representation of Image-Based Models

In this paper, we present a hybrid representation of image-based models combining the textured planes and the hierarchical points. Taking a set of depth images as input, our method starts from classifying input pixels into two categories, indicating the planar and non-planar surfaces respectively. For the planar surfaces, the geometric coefficients are reconstructed to form the uniformly sampled textures. For nearly planar surfaces, some textured planes, called lumiproxies, are constructed to represent the equivalent visual appearance. The Hough transform is used to find the positions of these textured planes, and optic flow measures are used to determine their textures. For remaining pixels corresponding to the non-planar geometries, the point primitive is applied, reorganized as the OBB-tree structure. Then, texture mapping and point splatting are employed together to render the novel views, with the hardware acceleration.

An Image-Based Virtual Reality Prototype System

The most important goal of virtual reality is to create a virtual world by computers where users are allowed to view the environment and control the virtual objects interactively. naditionally virtual reality systems use 3D computer graphics to model and render a virtual environment in real time. However, this approach usually requires laborious modeling and expensive special-purpose rendering hardware. Image-based rendering is a new approach in composing a virtual environment in which a set of panoramic images is used to compose the virtual environment and walking in the space is accomplished by 'hopping' to different panoramic points. This paper introduces an experimeatal image-based VR system. The techniques utilized in the system, in particular the authoring and interactive control tools of the system, are described in detail.

Numerical calculations Of effective thermal conductivity of porous ceramics by image-based finite element method

The effective thermal conductivity of heterogeneous or composite materials is an essential physical parameter of materials selection and design for specific functions in science and engineering. The effective thermal conductivity is heavily relied on the fraction and spatial distribution of each phase. In this work, image- based finite element method （FEM） was used to calculate the effective thermal conductivity of porous ceramics with different pore structures. Compared with former theoretical models such as effective media theory （EMT） equation and parallel model, image-based FEM can be applied to a large variety of material systems with a relatively steady deviation. The deviation of image-based FEM computation mainly comes from the difference between the two dimensional （2D） image and the three dimensional （3D） structure of the real system, and an experiment was carried out to confirm this assumption. Factors influencing 2D and 3D effective thermal conductivities were studied by FEM to illustrate the accuracy and application conditions of image-based FEM.

A Brief Introduction of the Achievements of Key Project Image-based Modeling and Rendering for Virtual Reality Applications

1. Background The virtual reality （VR） technology is now at the frontier of modern information science. VR is based on computer graphics, computer vision, and other fresh air topics in today＇s computer technology. Nowadays the VR technology has been applied successfully in variety of fields such as military simulation, industry, medical training and visualization, environment protection and entertainment.

Development of a Three-Dimensional Multiscale Octree SBFEM for Viscoelastic Problems of Heterogeneous Materials

The multiscale method provides an effective approach for the numerical analysis of heterogeneous viscoelastic materials by reducing the degree of freedoms(DOFs).A basic framework of the Multiscale Scaled Boundary Finite Element Method(MsSBFEM)was presented in our previous works,but those works only addressed two-dimensional problems.In order to solve more realistic problems,a three-dimensional MsSBFEM is further developed in this article.In the proposed method,the octree SBFEM is used to deal with the three-dimensional calculation for numerical base functions to bridge small and large scales,the three-dimensional image-based analysis can be conveniently conducted in small-scale and coarse nodes can be flexibly adjusted to improve the computational accuracy.Besides,the Temporally Piecewise Adaptive Algorithm(TPAA)is used to maintain the computational accuracy of multiscale analysis by adaptive calculation in time domain.The results of numerical examples show that the proposed method can significantly reduce the DOFs for three-dimensional viscoelastic analysis with good accuracy.For instance,the DOFs can be reduced by 9021 times compared with Direct Numerical Simulation(DNS)with an average error of 1.87%in the third example,and it is very effective in dealing with three-dimensional complex microstructures directly based on images without any geometric modelling process.

An Efficient Indoor Localization Based on Deep Attention Learning Model

Indoor localization methods can help many sectors,such as healthcare centers,smart homes,museums,warehouses,and retail malls,improve their service areas.As a result,it is crucial to look for low-cost methods that can provide exact localization in indoor locations.In this context,imagebased localization methods can play an important role in estimating both the position and the orientation of cameras regarding an object.Image-based localization faces many issues,such as image scale and rotation variance.Also,image-based localization’s accuracy and speed(latency)are two critical factors.This paper proposes an efficient 6-DoF deep-learning model for image-based localization.This model incorporates the channel attention module and the Scale PyramidModule(SPM).It not only enhances accuracy but also ensures the model’s real-time performance.In complex scenes,a channel attention module is employed to distinguish between the textures of the foregrounds and backgrounds.Our model adapted an SPM,a feature pyramid module for dealing with image scale and rotation variance issues.Furthermore,the proposed model employs two regressions(two fully connected layers),one for position and the other for orientation,which increases outcome accuracy.Experiments on standard indoor and outdoor datasets show that the proposed model has a significantly lower Mean Squared Error(MSE)for both position and orientation.On the indoor 7-Scenes dataset,the MSE for the position is reduced to 0.19 m and 6.25°for the orientation.Furthermore,on the outdoor Cambridge landmarks dataset,the MSE for the position is reduced to 0.63 m and 2.03°for the orientation.According to the findings,the proposed approach is superior and more successful than the baseline methods.

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

Artificial intelligence in gastric cancer: Application and future perspectives

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe,with a 5-year survival rate of less than 40%.In recent years,several applications of artificial intelligence(AI)have emerged in the gastric cancer field based on its efficient computational power and learning capacities,such as imagebased diagnosis and prognosis prediction.AI-assisted diagnosis includes pathology,endoscopy,and computerized tomography,while researchers in the prognosis circle focus on recurrence,metastasis,and survival prediction.In this review,a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed,Embase,Web of Science,and the Cochrane Library.Thereby the current status of AI-applications was systematically summarized in gastric cancer.Moreover,future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

Compressive strength of concrete is a significant factor to assess building structure health and safety.Therefore,various methods have been developed to evaluate the compressive strength of concrete structures.However,previous methods have several challenges in costly,time-consuming,and unsafety.To address these drawbacks,this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network(DCNN).The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy.The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.The experimental results indicated a root mean square error(RMSE)value of 3.56(MPa),demonstrating a strong feasibility that the proposed model can be utilized to predict the concrete strength with digital images of their surfaces and advantages to overcome the previous limitations.This experiment contributed to provide the basis that could be extended to future research with image analysis technique and artificial neural network in the diagnosis of concrete building structures.