Clinical application of improved 2D computer-assisted fluoroscopic navigation through simulating a 3D vertebrae image to guide pedicle screw internal fixation

Objective To study the effect of using improved 2D computer-assisted fluoroscopic navigation through simulating 3D vertebrae image to guide pedicle screw internal fixation.Methods Posterior pedicle screw internal fixation,distraction

Imaging the crystal orientation of 2D transition metal dichalcogenides using polarization-resolved second-harmonic generation

We use laser-scanning nonlinear imaging microscopy in atomically thin transition metal dichalcogenides(TMDs)to reveal information on the crystalline orientation distribution,within the 2D lattice.In particular,we perform polarization-resolved second-harmonic generation(PSHG)imaging in a stationary,raster-scanned chemical vapor deposition(CVD)-grown WS2 flake,in order to obtain with high precision a spatially resolved map of the orientation of its main crystallographic axis(armchair orientation).By fitting the experimental PSHG images of sub-micron resolution into a generalized nonlinear model,we are able to determine the armchair orientation for every pixel of the image of the 2D material,with further improved resolution.This pixel-wise mapping of the armchair orientation of 2D WS2 allows us to distinguish between different domains,reveal fine structure,and estimate the crystal orientation variability,which can be used as a unique crystal quality marker over large areas.The necessity and superiority of a polarization-resolved analysis over intensity-only measurements is experimentally demonstrated,while the advantages of PSHG over other techniques are analysed and discussed.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.

Auspicious Patterns and 3D Models Applied in the Creation of Personalized Products

Auspicious patterns are an important manifestation of traditional crafts aesthetics for Chinese culture, and it not only exhibits the clever tricks of folk art, showing more personality and characteristics of Chinese culture in the humanities and arts aesthetic concerns. It shows the traditional aesthetics, based on the harmonious and success, constructed by intelligence and humbleness, shaped by symmetry and balance. This thesis contains two topics: they are the 2D image materialization and the 3D model flattening. First is analyzing the image of the auspicious pattern, and transformed the 2D image into a solid model. The second is through the mathematical operation skills of the geometric model, the existing auspicious 3D model of the triangular mesh is scaled, appropriately rotated and divided to form a flattening model of different visual effects. Finally, these models by means of other modeling software were combined into a new 3D model, then through the 3D printer to quickly print out part of the unique personalized products, to promote the natural beauty of traditional Chinese culture.

3D laparoscopic anatomical hepatectomy guided by 2D real-time indocyanine green fluorescence imaging for hepatocellular carcinoma

Laparoscopic anatomical hepatectomy(LAH)for patients with hepatocellular carcinoma(HCC)has been advocated by many surgeons in the hope of producing better oncological outcomes.Two recent techniques,3D laparoscopic system and 2D real-time indocyanine green fluorescence imaging(r-ICG)guidance,are benefit for improving the operative precision of LAH in different aspects.However,these two techniques cannot be applied concomitantly because of the technical limitation.Although a new modern laparoscopic system with both 3D and indocyanine green(ICG)imaging mode has been designed,it has not been listed in many countries including China.Thus,we design a new procedure to perform the 3D LAH with 2D r-ICG guidance for HCCs with conventional laparoscopic systems.In this procedure,both 3D and 2D laparoscopic systems were used.A total of 11 patients with HCC received 3D laparoscopic right posterior sectionectomy(LRPS)with 2D r-ICG guidance.The right posterior Glissonian pedicle was clamped under the 3D vision.Then ICG solution was then intravenously administrated.The liver parenchyma was transected under the 3D vision and guided by 2D ICG vision simultaneously.There was no severe complications(Clavien-Dindo≥III)and operation related death.The 90-day mortality was also nil.By using this procedure,the advantages of two techniques,3D laparoscopic system and 2D r-ICG guidance,were combined so that LAH could be performed with more precision.However,it should be validated in more studies.

Fast X-ray imaging beamline at SSRF

The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.

A New Variational Model with Dual Level Set Functions for Selective Segmentation

In this paper we present a selective segmentation model using a dual level set variational formulation.Our variational model aims to segment all objects with one level set function(global)and the selected object,which is the closest to the geometric constraints(markers),with another level set(local).It is a combination of edge detection,markers distance function and active contour without edges.Experimental results show that our model is more robust than previous work.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.