Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

Limited by the dynamic range of the detector,saturation artifacts usually occur in optical coherence tomography(OCT)imaging for high scattering media.The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images.We proposed a deep learning-based inpainting method of saturation artifacts in this paper.The generation mechanism of saturation artifacts was analyzed,and experimental and simulated datasets were built based on the mechanism.Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs.The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility,strong generalization,and robustness.

Common Artifacts and Remedies in Histological Preparations

The importance of the accuracy of preparing biological specimen as histological sections that can be examined under a microscope lies in reflecting a true image of the tissue that includes all its components, which are used in scientific research or for the purpose of diagnosing various diseases of the body. Despite this, some cellular structures within the tissue may suffer from some alterations that result from the appearance of defects during any stage of preparing these microscopic sections, which alter or interfere with the precise cellular structures and morphology that constitute the tissue and thus give a different image for tissue features and cause confusion in the work histopathologist in the diagnosis. There are several reasons that can cause a misdiagnosis of the sample that occurs during the surgical separation process or after separation during the stages of microscopic preparation techniques from fixation stage, tissue processing, embedding or microtomy, staining until mounting procedures. The constant need to identify these defects and their causes in addition to try to reduce them is one of the biggest challenges evident in pathology laboratories. Therefore, this study aims to review the most common defects that occur in any stage of tissue processing, with an explanation of their causes and appropriate ways to avoid them.

GPU-accelerated OCT imaging: Real-time data processing and artifact suppression for enhanced monitoring of 3D bioprinted tissues and vascular-like networks

Optical coherence tomography(OCT)imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues.However,it still faces the following challenges:including data processing speed,image quality,and improvements in three-dimensional(3D)visualization effects.OCT technology,especially functional imaging techniques like optical coherence tomography angiography(OCTA),requires a long acquisition time and a large data size.Despite the substantial increase in the acquisition speed of swept source optical coherence tomography(SS-OCT),it still poses significant challenges for data processing.Additionally,during in situ acquisition,image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis.Firstly,a customized frequency domainfilter with anti-banding suppression parameters was designed to suppress artifact noises.Then,this study proposed a graphics processing unit(GPU)-based real-time data processing pipeline for SS-OCT,achieving a measured line-process rate of 800 kHz for 3D fast and high-quality data visualization.Furthermore,a GPU-based realtime data processing for CC-OCTA was integrated to acquire dynamic information.Moreover,a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials,with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network.OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation.Therefore,GPU-based OCT enables real-time processing and visualization with artifact suppression,making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.

Fetal MRI Artifacts: Semi-Supervised Generative Adversarial Neural Network for Motion Artifacts Reducing in Fetal Magnetic Resonance Images

This study addresses challenges in fetal magnetic resonance imaging (MRI) related to motion artifacts, maternal respiration, and hardware limitations. To enhance MRI quality, we employ deep learning techniques, specifically utilizing Cycle GAN. Synthetic pairs of images, simulating artifacts in fetal MRI, are generated to train the model. Our primary contribution is the use of Cycle GAN for fetal MRI restoration, augmented by artificially corrupted data. We compare three approaches (supervised Cycle GAN, Pix2Pix, and Mobile Unet) for artifact removal. Experimental results demonstrate that the proposed supervised Cycle GAN effectively removes artifacts while preserving image details, as validated through Structural Similarity Index Measure (SSIM) and normalized Mean Absolute Error (MAE). The method proves comparable to alternatives but avoids the generation of spurious regions, which is crucial for medical accuracy.

Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

This study presents an energy consumption(EC)forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning(FTL).To predict the EC of manufacturing products,particularly from scale-down to scale-up,a general paradigm was first developed by categorizing the overall process into three main sub-steps.The operating electrical power was further formulated as a combinatorial function,based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC.Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power.Considering the interconnections among these factors,the outputs of the neural networks were blended and fused to jointly predict the electrical power.Most innovatively,large artifacts can be decomposed into timedependent laser-scanning trajectories,which can be further transformed into fusionable information via neural networks,inspired by large language model.Accordingly,transfer learning can deal with either scale-down or scale-up forecasting,namely,FTL with scalability within artifact structures.The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion.The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%.The melting fusion quality was examined using metallographic diagrams.The proposed FTL framework can forecast the EC of scaled structures,which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

基于注意力机制的高光谱图像降维在纸质文物霉斑识别的研究

纸质文物作为文物传承的重要工具,用于记录不同时期人类历史及人文风貌,其在保存过程中极易受到霉菌等微生物的侵害。霉菌会加速纤维素的降解,在纸张表面生成霉斑,并且散落的孢子会随空气流动大范围传播,增加其他纸质文物发生霉变的风险。因此,定期对纸质文物进行霉斑检测对了解纸质文物现状和纸质文物修复至关重要。高光谱成像技术是一种非接触性、非破坏性的检测技术,能同时获得空间数据和光谱数据,与计算机技术结合可以实现纸质文物的大批次实时无损检测。针对黑曲霉这一广泛出现的霉菌,提出一种基于注意力机制的高光谱数据降维方法,通过采集其高光谱数据,实现了高光谱冗余数据的自适应预处理。采集了来自重庆中国三峡博物馆提供的20份纸质文物黑曲霉霉斑样本,使用ENVI软件分析得出在413~855 nm波段范围内,黑曲霉霉斑感染区域和健康区域的平均光谱曲线,平均反射率差异明显;在855~1021 nm波段范围内,黑曲霉霉斑感染区域和墨迹区域的平均光谱曲线,平均反射率差异明显。文中将所提出方法与传统主成分分析和独立成分分析预处理方法分别处理原始高光谱数据,并将结果在经典U-Net、SegNet、DeepLabV3+和PSPNet四个语义分割网络上进行了对比。结果表明,该算法预处理的数据在U-Net和SegNet经典网络中有明显优势,相较于主成分分析法和独立成分分析法,霉斑识别精度取得了较大提升达到89.49%和88.46%,验证了本文所提出算法的有效性,为文物保护领域提供有效的支撑和新的思路。

诠释学视域下技术人工物结构—功能关系研究

自荷兰学者克劳斯提出技术人工物结构—功能关系的“逻辑鸿沟”问题以来,学者们试图从逻辑关系、认识论和本体论等视域来跨越鸿沟,但都未能建立起技术人工物结构—功能之间的因果性关系.根据海德格尔理解与存在同一的思想,人作为结构与功能相互指向的中间载体是不可或缺的,所以技术人工物结构—功能是一种诠释学的关系.诠释学的完整内涵要以阐释、应用和说明形成统一体.在诠释学内涵的基础上,揭示了技术人工物结构—功能之间的诠释过程,为跨越技术人工物结构—功能关系的“逻辑鸿沟”问题提出了新的视角.

考虑结构和展柜实际动力特性的浮放文物地震响应分析

目前评估文物地震安全性时,未充分考虑博物馆和展柜的实际动力特性对文物地震运动响应的影响。对某实际博物馆进行了现场环境振动测试,识别得到了符合现状的结构动力特性;对某实际独立展柜进行了现场锤击测试,识别得到了其自振频率。建立了结构实测动力特性修正前后的2种线性有限元模型、不考虑和考虑结构周期折减的2种非线性有限元模型,分析了不同地震波下4种模型中楼层相对于地面的加速度放大效应;建立了展柜的有限元模型,分析了不同楼层波下展柜相对于楼层的加速度放大效应;考虑文物与展柜台面间接触界面的非线性,建立了纯滑移、纯摇摆和滑移-摇摆耦合文物的有限元模型,并与理论运动方程所得响应进行了对比,分析了不同结构模型中位于不同楼层的展柜对浮放文物运动响应的影响。结果表明:相比另外3种模型,经实际动力特性修正后的线性结构模型中,展柜相对楼层的峰值加速度放大系数均值在小震和中震下均为最大,且大部分情况下该结构模型中文物的响应最大。

基于Artifact快照序列的行为一致性检测方法

Artifact行为的一致性检测,是在流程建模、运行之后亟待解决的关键问题之一.针对现有一致性检测技术忽略数据操作方面检测的问题,提出了一种基于Artifact快照序列的行为一致性检测方法.首先,利用全序Artifact快照序列定义了Artifact的行为模式,该行为模式不仅体现了服务的运行轨迹,也描述出了Artifact数据属性赋值的状态变化;然后,将Artifact行为一致性检测问题转换为语言可判定问题,证明了该问题是一个可判定问题,该过程中,设计一台判定该语言的图灵机作为一致性验证模型,该模型不仅检测了Artifact生命周期中服务路径的一致性,同时也检测了生命周期中Artifact属性赋值的正确性;进一步地,利用服务-快照关联矩阵的等价转换,给出了行为一致性量化指标中确切度的精确计算方法;最后,通过实例分析及实验对所提出的方法进行了验证.

ArtiFlow中artifact生命周期的可满足性问题

以数据为中心是业务过程管理技术发展的一个新趋势.artifact是记录业务过程的数据实体,围绕artifact的业务过程管理正在成为业务过程管理研究的一个热点.生命周期是artifact的一个重要特征,业务过程能否满足对artifact生命周期的定义是业务过程设计中需要验证的一个重要特性.本文从artifact属性赋值顺序的角度,基于Petri网定义artifact的生命周期树.采用ArtiFlow建立业务过程模型,在ArtiFlow模型中根据业务规则提取artifact状态变化树,与artifact生命周期树进行路径比较,验证业务过程模型中artifact生命周期的可满足性.给出相应的算法并分析了算法的复杂度.

Artifact的有效性问题研究

为及时发现业务活动对数据的非法操作,将违规实例监测问题归结为Artifact有效性检查问题,基于时间Petri网提出Artifact业务要求模型,从属性赋值顺序的角度给出Artifact在生命周期中的状态变化及时间要求。证明了在给出业务要求模型并求出状态类图的条件下,该问题是多项式时间问题。研究结果表明,该方法支持业务流程中Artifact的时间建模,为业务流程中验证Artifact的有效性提供了新的途径。

面向Artifact的业务流程行为相似性度量方法

面向Artifact的业务流程是以数据为中心的业务流程的代表.与传统以过程为中心的业务流程相似,为了更好的对流程模型进行流程检索、流程挖掘等操作,计算流程间的相似性或距离是一个关键的问题.给出一种面向Artifact的业务流程行为相似性度量方法.首先,通过测量流程模型之间关键Artifact的相似性来评估流程处理的核心业务数据的相似度.其次,根据关键Artifact生命周期特性,测量任务执行路径中任务依赖关系的相似性.最后,测量生命周期中关键Artifact属性赋值序列的相似性.理论和实例分析表明,该方法是一个有效的相似性度量方法.

ArtiFlow Designer:一种面向Artifact的业务流程设计工具

ArtiFlow是一种以数据为中心的业务流程描述模型,如何管理ArtiFlow是面向Artifact业务流程管理中的一个关键问题.ArtiFlow的设计方法和设计工具是ArtiFlow管理的基础,首先在介绍Artifact和Arti-fact系统的基础上给出了以业务流程中关键Artifact及其生命周期为出发点的ArtiFlow设计方法;其次描述了ArtiFlow模型中各元素的含义,并给出了ArtiFlow模型的形式化定义;最后设计开发出了ArtiFlow的设计工具——ArtiFlow Designer.

一种以Artifact为中心的多业务流程协同监控方法

多业务流程协同监控是通过监控合作伙伴的行为,保证可以灵活、动态地选择最优合作伙伴,确保企业利益最大化的一种有效方法。已有的方法在监控过程中忽略了业务流程数据的重要性,一定程度上降低了监控信息质量和可利用性。因此,本文提出一种以Artifact为中心的多业务流程协同监控方法。首先,给出了以Artifact为中心的业务流程协同模型及Artifact实例协同快照定义。其次,采用快照日志挖掘获得候选以Artifact为中心的业务流程协同模型,然后,根据蚁群优化算法在候选流程模型中获取最优流程服务协同路径。最后,通过实例分析验证了方法的可行性。

以Artifact为中心的业务流程扩展二部图模型及其相似性度量方法

为了保证计算结果的准确性和有效性,提出一种以Artifact为中心的业务流程扩展二部图模型及其相似性度量方法。首先提出一个用于描述以Artifact为中心的业务流程的扩展二部图模型,将流程模型匹配转换为图匹配问题;其次提出一种利用矩阵转换操作来度量图编辑距离的方法;在扩展二部图模型的基础上结合属性划分技术,分别提出服务节点和仓库节点行为相似度计算方法;设计实现了以Artifact为中心的业务流程扩展二部图的图匹配算法。理论分析和实验结果表明,该算法在查询时间和执行效率上优于现有算法,具有较高的查准率和检索质量。

面向Artifact的业务过程模型

提出一种面向Artifact的业务过程形式化模型。利用谓词/变迁Petri网建模的优势,根据Artifact的特点,用半结构化数据描述Artifact,以表示Artifact结构的文档类型定义作为静态谓词,通过一种操作XML的一阶逻辑语言描述变迁。实验表明,该模型能有效分析Artifact的可达性、持久性和唯一性等问题。

Ar/T-Net:一种面向Artifact的业务过程概念模型

以数据为中心是业务过程管理发展的新趋势,面向Artifact的业务过程管理是一种具有代表性的以数据为中心的业务过程管理技术。概念模型设计是面向Artifact的业务过程设计的关键步骤,但目前还没有成型的建模和分析工具。利用谓词/变迁Petri-Net建模的优势,根据Artifact的特点,用嵌套关系模式定义Artifact的结构;以描述Artifact的嵌套关系模式作为谓词,提出了一种适合于概念模型设计的谓词/变迁Petri-Net,即Ar/T-Net。以可达性分析为例,讨论了Ar/T-Net的功能。

ArtiMate:一种以Artifact为中心的多粒度协作流程建模方法

业务流程是组合服务的主要表现形式之一.跨组织多方协作流程往往包含多重粒度,难以基于任何单一粒度建模.Proclets方法将多粒度单体流程分解为一组交互协作的单粒度流程,以实例的基数、多重性来约束其间的交互关系.然而,在此类方法中如何体现流程业务目标与业务进展,支持复杂且多变的协作关系,准确刻画模型的执行语义等问题尚缺乏有效的建模技术.提出一种以Artifact为中心的多粒度协作流程建模方法 Arti Mate:继承proclets的粒度分解思路但以Artifact为中心建模各单粒度流程artilet,利于刻画业务目标,追踪业务进展;解耦交互机制和交互策略,以丰富、插件式交互策略polilet连接较为稳定的artilet,复用实质性业务流程并适应协作需求变化;以着色Petri网描述的Arti Mate的执行语义,有利于建模方法的实现和仿真确认.论文以国家海洋局东海分局会签类应用为案例,检验了Arti Mate方法的可行性和有效性.

Sinogram fusion-based metal artifact correction method

To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain proportion.The final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT image.Experiments on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.