Image Data Management Research Articles

Web-Based DICOM Viewers: A Survey and a Performance Classification.

The standard for managing image data in healthcare is the DICOM (Digital Imaging and Communications in Medicine) protocol. DICOM web-viewers provide flexible and accessible platforms for their users to view and analyze DICOM images remotely. This article presents a comprehensive evaluation of various web-based DICOM viewers, emphasizing their performance in different rendering scenarios, browsers, and operating systems. The study includes a total of 16 web-based viewers, of which 12 were surveyed, and 7 were compared performance-wise based on the availability of an online demo. The criteria for examination include accessibility features, such as available information or requirements for usage, interface features, such as loading capabilities or cloud storage, two-dimensional (2D) viewing features, such as the ability to perform measurements or alter the viewing window, and three-dimensional (3D) viewing features, such as volume rendering or secondary reconstruction. Only 4 of the viewers allow for the viewing of local DICOM files in 3D (other than MPR(Multiplanar reconstruction)). Premium software offers a large amount of features with overall good performance. One of the free alternatives demonstrated the best efficiency in both 2D and 3D rendering but faces challenges with missing 3D rendering features in its interface, which is still in development. Other free options exhibited slower performance, especially in 2D rendering but have more ready-to-use features on their web app. The evaluation also underscores the importance of browser choice, with some browsers performing much better than the competition, and highlights the significance of hardware when dealing with rendering tasks.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.

Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing

High-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.

OBIA: An Open Biomedical Imaging Archive

With the development of artificial intelligence (AI) technologies, biomedical imaging data play an important role in scientific research and clinical application, but the available resources are limited. Here we present Open Biomedical Imaging Archive (OBIA), a repository for archiving biomedical imaging and related clinical data. OBIA adopts five data objects (Collection, Individual, Study, Series, and Image) for data organization, and accepts the submission of biomedical images of multiple modalities, organs, and diseases. In order to protect personal privacy, OBIA has formulated a unified de-identification and quality control process. In addition, OBIA provides friendly and intuitive web interfaces for data submission, browsing, and retrieval, as well as image retrieval. As of September 2023, OBIA has housed data for a total of 937 individuals, 4136 studies, 24,701 series, and 1,938,309 images covering 9 modalities and 30 anatomical sites. Collectively, OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world. OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.

Efficient Inspection of Civil Engineering Structures for Railways and Roads Using Images and GNSS

As structures deteriorate and engineers age, efficient maintenance and management of civil engineering structures becomes increasingly important. Alternative inspection of civil engineering structures using images is being recognized as an efficient inspection method. However, acquiring images of large and extensive civil engineering structures is time and equipment-intensive, which hinders mobility and is consequently not efficient. In addition, the management of existing civil engineering structures is highly specialized and lacks scalability, which means that significant effort is required to associate images with civil engineering structures. Therefore, we decided to acquire images of large and extensive civil engineering structures with video cameras to improve mobility and efficiency. In addition, as a key to linking images and civil engineering structures, we developed a technology to record latitude and longitude information as audio data in a video using GNSS (Global Navigation Satellite System) + dual-frequency RTK (Real Time Kinematic) This facilitates the rapid comparison of images and management data for existing structures with latitude and longitude information, as well as image data management in chronological order. This results in improved productivity in image-based inspection. In addition, AI processing of image data has made it possible to sample deformed areas and analyze geographical characteristics. These combined technologies have raised expectations for preventive maintenance of structures using images.

VoDEx: a Python library for time annotation and management of volumetric functional imaging data.

In functional imaging studies, accurately synchronizing the time course of experimental manipulations and stimulus presentations with resulting imaging data is crucial for analysis. Current software tools lack such functionality, requiring manual processing of the experimental and imaging data, which is error-prone and potentially non-reproducible. We present VoDEx, an open-source Python library that streamlines the data management and analysis of functional imaging data. VoDEx synchronizes the experimental timeline and events (e.g. presented stimuli, recorded behavior) with imaging data. VoDEx provides tools for logging and storing the timeline annotation, and enables retrieval of imaging data based on specific time-based and manipulation-based experimental conditions. VoDEx is an open-source Python library and can be installed via the "pip install" command. It is released under a BSD license, and its source code is publicly accessible on GitHub (https://github.com/LemonJust/vodex). A graphical interface is available as a napari-vodex plugin, which can be installed through the napari plugins menu or using "pip install." The source code for the napari plugin is available on GitHub (https://github.com/LemonJust/napari-vodex). The software version at the time of submission is archived at Zenodo (version v1.0.18, https://zenodo.org/record/8061531).

Creating the Future of (Endoluminal) GI Interventions.

Major innovation into how we pursue diagnosis and therapies for gastrointestinal (GI) diseases is urgently needed to seek better, less invasive, and less costly innovations in diagnostic and therapeutic interventions in the GI tract. Learning from prior paradigm shifts in cardiac and vascular we present here several initial steps we have undertaken to follow the endoluminal path, using advanced imaging methods, including endoscopy, and data management with avoidance of entry into a body cavity when possible. We will review the benefit and ease of incorporating routine fluoroscopy with endoscopy to improve safety and efficiency. We describe the development of "hybrid" procedure rooms for GI interventions and rationale for their use. We also emphasize the importance of collaborating with interventional radiologists, software engineers, and data specialists. We predict major improvement in outcomes in both diagnosis and treatment will follow.

Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI

PurposeThe ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. MethodA 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. ResultsThe model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types. ConclusionsThe herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows.

A quality management system aiming to ensure regulatory-grade data quality in a glaucoma registry.

Disease/patient registries are underutilized despite their multiple advantages over clinical trials in the clinical evaluation of drugs, such as the capacity for long-term curation, provision of patient outcome data in routine clinical practice, and provision of benchmark data for comparison. Ensuring the fit-for-purpose quality of data generated from such registries is important to informing regulatory decision making. Here, we report the construction of a quality management system aiming to ensure regulatory-grade data quality for a registry of Japanese patients with glaucoma to evaluate long-term patient outcomes. The quality management system was established by reference to the risk-based approach in the ICH-E6 (R2) recommendations. The following three-component approach was taken: establishment of governance, computerized system validation (CSV), and implementation of risk assessment and control. Compliance of the system with the recommendations of regulatory guidelines relevant to use of the registry was assessed. Governance by academic collaboration was established. This was followed by the development of a total of 15 standard operating procedures, including CSV, data management, monitoring, audit, and management of imaging data. The data management system was constructed based on a data management plan, which specified data/paper flow and data management procedures. The electronic data capture (EDC) system was audited by an external vendor, and configured and validated using the V-model framework as recommended in the GAMP5 guideline. Informed consent, eligibility assessment and major ophthalmology measurements were determined as Critical to Quality (CTQ) factors. A total of 22 risk items were identified and classified into three categories, and operationalized in the form of a risk control plan, which included training sessions and risk-based monitoring. The glaucoma registry addressed most quality recommendations in official guidelines issued by multiple health authorities, although two recommendations were not met. We established and configured a quality management system for a glaucoma registry to ensure fit-for-purpose data quality for regulatory use, and to curate long-term follow-up data of glaucoma patients in a prospective manner.

How do sonographers improve access to cardiology care? An outpatient study

We report our experience working with cardiac sonographers (SONO) in outpatient settings between April 2019 and December 2022. Our observation focused on 7 outpatient cardiac centers in the Auvergne Rhône Alpes region, 35,743 patients had at least one consultation between April 2019 and December 2022. In total, 23,657 patients an echocardiogram (ECHO) performed (66%). Seven SONOs were involved, supervised by 15 cardiologists. All ECHOs were technically performed based on a standard acquisition protocol. Data was saved on a dedicated cloud-based Picture Archiving and Communication System (PACS) allowing the cardiologist to check the image quality, results and complete the report, automatically included in the electronic health record. Out of a total of 25,686 ECHO (i.e., an average of 1.1 ECHO/patient), ∼19,800 (77%) were performed by SONOs (range from 72% to 92% depending on the medical center). In total, ∼2000 ECHO (∼10%) required technical expertise and additional acquisition of images by the referring cardiologist (range from 5% to 25% depending on the experience level of the SONO). Based on a study sample, the average ECHO acquisition time was 12 ± 7 minutes. Thus, over the entire period of 33-month period, the total medical time saved was estimated up to 4,700 hours. Teamwork is mandatory to improve access to outpatient care in cardiology. Working with cardiac sonographers is efficient and saves cardiologists up to 700 hours/SONO/year of medical time. This organizational workflow should be paired with dedicated imaging data management systems and HER (Fig. 1).

Development of a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository.

To establish a hepatocellular carcinoma imaging database and structured imaging reports based on PACS, HIS, and repository. This study was approved by the Institutional Review Board. The steps of establishing the database are as follows: 1) According to the standards required for the intelligent diagnosis of HCC, it was attempted to design the corresponding functional modules after analyzing the requirements; 2) Based on client/server (C/S) mode, 3-tier architecture model was adopted. A user interface (UI) could receive data entered by users and show handled data. Business logic layer (BLL) could process the business logic of the data, and data access layer (DAL) could save the data in the database. The storage and management of HCC imaging data could be realized by the SQLSERVER database management software, and Delphi and VC++ programming languages were used. The test results showed that the proposed database could swiftly obtain the pathological, clinical, and imaging data of HCC from the picture archiving and communication system (PACS) and hospital information system (HIS), and perform data storage and visualization of structured imaging reports. According to the HCC imaging data, liver imaging reporting and data system (LI-RADS) assessment, standardized staging, and intelligent imaging analysis were carried out on the high-risk population to establish a one-stop imaging evaluation platform for HCC, strongly supporting clinicians in the diagnosis and treatment of HCC. The establishment of a HCC imaging database can not only provide a huge amount of imaging data for the basic and clinical research on HCC, but also facilitate the scientific management and quantitative assessment of HCC. Besides, a HCC imaging database is advantageous for personalized treatment and follow-up of HCC patients.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae.

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background: Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods: An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results: The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusion: The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey.

Background Knowing the needs of the bioimaging community with respect to research data management (RDM) is essential for identifying measures that enable adoption of the FAIR (findable, accessible, interoperable, reusable) principles for microscopy and bioimage analysis data across disciplines. As an initiative within Germany's National Research Data Infrastructure, we conducted this community survey in summer 2021 to assess the state of the art of bioimaging RDM and the community needs. Methods An online survey was conducted with a mixed question-type design. We created a questionnaire tailored to relevant topics of the bioimaging community, including specific questions on bioimaging methods and bioimage analysis, as well as more general questions on RDM principles and tools. 203 survey entries were included in the analysis covering the perspectives from various life and biomedical science disciplines and from participants at different career levels. Results The results highlight the importance and value of bioimaging RDM and data sharing. However, the practical implementation of FAIR practices is impeded by technical hurdles, lack of knowledge, and insecurity about the legal aspects of data sharing. The survey participants request metadata guidelines and annotation tools and endorse the usage of image data management platforms. At present, OMERO (Open Microscopy Environment Remote Objects) is the best known and most widely used platform. Most respondents rely on image processing and analysis, which they regard as the most time-consuming step of the bioimage data workflow. While knowledge about and implementation of electronic lab notebooks and data management plans is limited, respondents acknowledge their potential value for data handling and publication. Conclusions The bioimaging community acknowledges and endorses the value of RDM and data sharing. Still, there is a need for information, guidance, and standardization to foster the adoption of FAIR data handling. This survey may help inspiring targeted measures to close this gap.

Diagnostic accuracy and inter-reader reliability of the MRI Liver Imaging Reporting and Data System (version 2018) risk stratification and management system.

BackgroundHepatocellular carcinoma (HCC) can be diagnosed non-invasively, provided certain imaging criteria are met. However, the recent Liver Imaging Reporting and Data System (LI-RADS) version 2018 has not been widely validated.ObjectivesThis study aimed to evaluate the diagnostic accuracy and reader reliability of the LI-RADS version 2018 lexicon amongst fellowship trained radiologists compared with an expert consensus reference standard.MethodThis retrospective study was conducted between 2018 and 2020. A total of 50 contrast enhanced liver magnetic resonance imaging (MRI) studies evaluating focal liver observations in patients with cirrhosis, hepatitis B virus (HBV) or prior HCC were acquired. The standard of reference was a consensus review by three fellowship-trained radiologists. Diagnostic accuracy including sensitivity, specificity, positive predictive value (PPV), negative predictive values (NPV) and area under the curve (AUC) values were calculated per LI-RADS category for each reader. Kappa statistics were used to measure reader agreement.ResultsReaders demonstrated excellent specificities (88% – 100%) and NPVs (85% – 100%) across all LI-RADS categories. Sensitivities were variable, ranging from 67% to 83% for LI-RADS 1, 29% to 43% for LI-RADS 2, 100% for LI-RADS 3, 70% to 80% for LI-RADS 4 and 80% to 84% for LI-RADS 5. Readers showed excellent accuracy for differentiating benign and malignant liver lesions with AUC values > 0.90. Overall inter-reader agreement was ‘good’ (kappa = 0.76, p < 0.001). Pairwise inter-reader agreement was ‘very good’ (kappa ≥ 0.90, p < 0.001).ConclusionThe LI-RADS version 2018 demonstrates excellent specificity, NPV and AUC values for risk stratification of liver observations by radiologists. Liver Imaging Reporting and Data System can reliably differentiate benign from malignant lesions when used in conjunction with corresponding LI-RADS management recommendations.

Variation of the Implementation of PACS Functions and Image Data Management: Questionnaire Survey in Hokkaido Prefecture

The image management function of picture archiving and communication system (PACS) plays a very important role while electronically preserving medical records. However, details of these mechanisms vary from manufacturer to manufacturer, which could be a problem if system specifications or image management functions are not confirmed by a user during system installation or operation. The purpose of this research was to record the differences in PACS settings and image data management in hospitals. A questionnaire survey was conducted in 261 hospitals having the PACS system in Hokkaido prefecture, Japan. Questionnaire contents consisted of a face sheet and the following two items: 1) PACS image deletion method, 2) rules regarding the PACS receiving an image of data that was already registered. Results of the questionnaire survey showed that the image deletion method in the PACS server, and the overwriting rule in case of existent images being re-transmitted to the server were different between different hospitals. Furthermore, these variations were not dependent on the vendors or manufacturers, but on the settings of each hospital. Since the image management method of PACS is not standardized, we were considered to be needed more attention and appropriate regulation for safe management of electronic medical records as per the national guideline.

XNAT-PIC: Extending XNAT to Preclinical Imaging Centers

Molecular imaging generates large volumes of heterogeneous biomedical imagery with an impelling need of guidelines for handling image data. Although several successful solutions have been implemented for human epidemiologic studies, few and limited approaches have been proposed for animal population studies. Preclinical imaging research deals with a variety of machinery yielding tons of raw data but the current practices to store and distribute image data are inadequate. Therefore, standard tools for the analysis of large image datasets need to be established. In this paper, we present an extension of XNAT for Preclinical Imaging Centers (XNAT-PIC). XNAT is a worldwide used, open-source platform for securely hosting, sharing, and processing of clinical imaging studies. Despite its success, neither tools for importing large, multimodal preclinical image datasets nor pipelines for processing whole imaging studies are yet available in XNAT. In order to overcome these limitations, we have developed several tools to expand the XNAT core functionalities for supporting preclinical imaging facilities. Our aim is to streamline the management and exchange of image data within the preclinical imaging community, thereby enhancing the reproducibility of the results of image processing and promoting open science practices.

A data management infrastructure for the integration of imaging and omics data in life sciences

BackgroundAs technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management.ResultsWe propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities.ConclusionsWe describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.

Smart City's Security Model for Management of Image Data on Cloud

In modern world of advancement, cloud technology is taking its grip. COVID-19 also enhance the use of the cloud services. The cloud concept comes with the new advancement of technology, but with that data breaches and data hacks are also increasing. In the cloud-based environment, we suggested the model of security enhancement which, make use pf role-based security model where the files are shared according to the role and the access of such file is controlled using TPA validation and apart for that the concept of authentication of user is also suggested using the Pattern Lock based graphical system for pattern formation. The pattern obtained are validated using the various of online password or pattern strength validation tools, results which are obtained proves that pattern obtained through the proposed concept performs better against the attack.