Fiji plugins for qualitative image annotations: routine analysis and application to image classification

BackgroundEven though R is one of the most commonly used statistical computing environments, it lacks a graphical user interface (GUI) that appeals to students, researchers, lecturers, and practitioners in medicine and pharmacy for conducting standard data analytics. Current GUIs built on top of R, such as EZR and R-Commander, aim to facilitate R coding and visualization, but most of the functionalities are still accessed through a command-line interface (CLI). To assist practitioners of medicine and pharmacy and researchers to run most routines in fundamental statistical analysis, we developed an interactive GUI; i.e., MEPHAS, to support various web-based systems that are accessible from laptops, workstations, or tablets, under Windows, macOS (and IOS), or Linux. In addition to fundamental statistical analysis, advanced statistics such as the extended Cox regression and dimensional analyses including partial least squares regression (PLS-R) and sparse partial least squares regression (SPLS-R), are also available in MEPHAS.ResultsMEPHAS is a web-based GUI (https://alain003.phs.osaka-u.ac.jp/mephas/) that is based on a shiny framework. We also created the corresponding R package mephas (https://mephas.github.io/). Thus far, MEPHAS has supported four categories of statistics, including probability, hypothesis testing, regression models, and dimensional analyses. Instructions and help menus were accessible during the entire analytical process via the web-based GUI, particularly advanced dimensional data analysis that required much explanation. The GUI was designed to be intuitive for non-technical users to perform various statistical functions, e.g., managing data, customizing plots, setting parameters, and monitoring real-time results, without any R coding from users. All generated graphs can be saved to local machines, and tables can be downloaded as CSV files.ConclusionMEPHAS is a free and open-source web-interactive GUI that was designed to support statistical data analyses and prediction for medical and pharmaceutical practitioners and researchers. It enables various medical and pharmaceutical statistical analyses through interactive parameter settings and dynamic visualization of the results.

MotivationActin filaments (AFs) are dynamic structures that substantially change their organization over time. The dynamic behavior and the relatively low signal-to-noise ratio during live-cell imaging have rendered the quantification of the actin organization a difficult task.ResultsWe developed an automated image-based framework that extracts AFs from fluorescence microscopy images and represents them as networks, which are automatically analyzed to identify and compare biologically relevant features. Although the source code is freely available, we have now implemented the framework into a graphical user interface that can be installed as a Fiji plugin, thus enabling easy access by the research community.Availability and implementationCytoSeg 2.0 is open-source software under the GPL and is available on Github: https://github.com/jnowak90/CytoSeg2.0.Supplementary informationSupplementary data are available at Bioinformatics online.

Zooplankton is enormously diverse and fundamental group of microorganisms that exists in almost every freshwater body, determining its ecology and play a vital role in food chain. Considering the significance of zooplankton, the study of freshwater zooplankton is very essential which intensely relies on the classification of images. However, the routine manual analysis and classification is laborious, time consuming and expensive, and poses a significant challenge to experts. Thus, for recent decade much research is focused on the development of underwater imaging technologies and intelligent classification system of zooplankton. This work presents devotion to observation of freshwater zooplankton by designed underwater microscope and modeling the system for automatic classification among four different taxa. Unlike most of the existing zooplankton image classification systems, this model is trained on a comparatively small dataset collected from freshwater by designed underwater microscope. Transfer learning of pre-trained AlexNet Convolutional Neural Network (CNN) model proved to be a potential approach in the system design. Among four networks trained over two datasets, the best overall classification accuracy of up to 93.1%, comparable to other existing systems was achieved on test dataset (92.5% for Calanoid and Cyclopoid (Female), 90% for Cyclopoid (Male) and 97.5% for Daphnia). Graphical User Interface (GUI) of the model constructed on MATLAB, makes it easy for the users to collect images for building database, train network and to classify images of different taxa. Moreover, the designed system is adaptable to the addition of more classes in the future.

Heart monitoring system is a device to carry out electrocardiogram (ECG) test. It can diagnose human's heart either in normal or abnormal condition based on ECG analysis. However, most of the current ECG device is bulky, expensive and lack of a user-friendly graphical user interface (GUI) for user interaction. This paper describes an on-board GUI design based on capacitive touch-screen Liquid Crystal Display (LCD) display. It is integrated with the in-house design of ECG processing unit and arrhythmia classifier to form a complete System-on-Chip (SoC) based arrhythmia detector. The whole system is implemented on the Altera Video and Embedded Evaluation Kit with Multi-Touch (VEEK-MT) FPGA development board. The touch screen GUI acts as a front-end menu to capture user input and retrieve the offline ECG data stored in external SD card, as well as back-end result display in terms of arrhythmia classification result, ECG raw signal display, and R-R interval. Prototyping result shows that this system is light-weight, cost-effective and suitable to be user for different groups of user according to their knowledge background, including patient, nurse, physician and cardiologist.

The use of a TLC scanner can be regarded as a key step in high performance thin layer chromatography (HPTLC). Densitometric measurements transform the substance distribution on a TLC plate into digital computer data. Systems that allow quantitative measurements have been available for many years for either fluorescence or ultraviolet absorption measurements, while lately the reflection analysis mode for both types is the most common application. New scanning approaches are designed to aid the analyst who has common demands for TLC-densitometry without using special data, such as scanned images. Two examples that have been developed lately in the laboratories of the authors are described in this paper. These approaches were developed on the basis of current needs for analysts who employ TLC as a tool in research, as well as in routine analysis. One approach is aimed to support analysts in economically disadvantaged areas, where cost intensive apparatus is unsuitable but trace analysis by simple means is required. The other system, allows the spectral determination of chromatographic spots on TLC plates covering the ultraviolet and visible range, thus, revealing highly desired information for the analyst.

This paper presents an interactive graphics tool for the structural analysis of one‐dimensional element models, such as trusses, frames, and grillages. The focus is on the creation of a graphical user interface for a previously developed analysis solver. This graphical interface is entirely implemented in the MATLAB environment and features intuitive resources so that modeling and verification of results become easy tasks for students and engineers. The management of mouse events and the adopted solutions to basic computer graphics issues that arise from interactive modeling in two‐dimensional and three‐dimensional canvases are discussed, including the strategies to pick objects in spatial coordinates and to identify crossing elements. The result is a new educational software that proved to be a very useful application in structural analysis and introductory computer graphics courses for engineering. The article also presents examples of using the software as an educational tool.

Nowadays, touchable displays are widely used in most embedded systems, including mobile phones and other hand-held devices. Although interactive user interfaces are intuitively required for these systems, most of interactive graphical user interface implementations usually need lots of system resources. For low-tier embedded systems, we have presented that the text-based user interfaces are more suitable, as shown in our previous works. In this paper, we present an object-oriented approach to provide a full-scale text-based user interface scheme, especially on the low-tier embedded systems. Our application user interface implementation is very simple and intuitive, with few amount of computing resource. It naturally supports multiple overlapping windows, pop-up menus, pull-down menus, dialog boxes, and other user interface components, with graceful object-oriented application programming interfaces. We also provide large size text output for emphasis purposes. All the systems are now available as a software library, and can be used for wide range of applications.

The majority of industrial inspection activities across the globe involve considerable human efforts due to the requirements of robust detection abilities under varying lighting conditions, large field of view, higher speeds, and dynamic range of operation. In recent years, the motivation for integrating inspection and manufacturing operations on the shop floor has grown considerably to maximize the potential benefits of implementing Industry 4.0 initiatives. It is essential to digitize and integrate inspection of raw materials, in-process inventory, and finished components with manufacturing operations for achieving autonomous manufacturing goals. Machine vision offers an excellent alternative to manual inspection at a reduced cost, increased productivity, digital output, and negligible errors. However, its utility is restricted to the finished component inspection currently due to exorbitant equipment costs, requirements of specific lighting conditions, and a controlled operating environment. This paper presents the development of an integrated system combining a camera and ultrasonic sensor to develop a low-cost dimensional measurement system for inspection of raw material and in-process machined components in the shop floor setting. An indigenous inspection system is developed by integrating a camera, ultrasonic sensor, power source, and interactive user interface with onboard processing abilities. The augmentation of the ultrasonic sensor serves a dual purpose of eliminating the need for image calibration and dimensions measurement in the system. An interactive graphical user interface with acquisition trigger abilities and results display is developed using an open-source TkInter framework of the Python environment. A set of dimension measurements are performed for common raw material and in-process machined shapes using the developed setup. The comparison of results obtained using developed inspection setup and commercial machine vision system showed good agreement for common shapes. The efficacy of the developed system is validated for inspection of simple shapes with machining operations on the shop floor in the present work. The proposed system can be easily extended to inspect complex shapes such as gear profiles or camshafts as subsequent research work.

Unprecedented 4D spatiotemporal infant regional cerebral blood flow framework and region-specific physiology–function coupling across infancy were elucidated, highlighting strong physiology–function coupling specifically at the default-mode network to meet extraneuronal metabolic demand for network emergence.

A learning approach to knowledge-assisted image analysis and classification is proposed that combines global and local information with explicitly defined knowledge in the form of an ontology. The ontology specifies the domain of interest, its subdomains, the concepts related to each subdomain as well as contextual information. Support vector machines (SVMs) are employed in order to provide image classification to the ontology subdomains based on global image descriptions. In parallel, a segmentation algorithm is applied to segment the image into regions and SVMs are again employed, this time for performing an initial mapping between region low-level visual features and the concepts in the ontology. Then, a decision function, that receives as input the computed region-concept associations together with contextual information in the form of concept frequency of appearance, realizes image classification based on local information. A fusion mechanism subsequently combines the intermediate classification results, provided by the local- and global-level information processing, to decide on the final image classification. Once the image subdomain is selected, final region-concept association is performed using again SVMs and a genetic algorithm (GA) for optimizing the mapping between the image regions and the selected subdomain concepts taking into account contextual information in the form of spatial relations. Application of the proposed approach to images of the selected domain results in their classification (i.e., their assignment to one of the defined subdomains) and the generation of a fine granularity semantic representation of them (i.e., a segmentation map with semantic concepts attached to each segment). Experiments with images from the personal collection domain, as well as comparative evaluation with other approaches of the literature, demonstrate the performance of the proposed approach.

Scientists and regulators are often faced with complex decisions, where use of scarce resources must be prioritized using collections of diverse information. The Toxicological Prioritization Index (ToxPi™) was developed to enable integration of multiple sources of evidence on exposure and/or safety, transformed into transparent visual rankings to facilitate decision making. The rankings and associated graphical profiles can be used to prioritize resources in various decision contexts, such as testing chemical toxicity or assessing similarity of predicted compound bioactivity profiles. The amount and types of information available to decision makers are increasing exponentially, while the complex decisions must rely on specialized domain knowledge across multiple criteria of varying importance. Thus, the ToxPi bridges a gap, combining rigorous aggregation of evidence with ease of communication to stakeholders. An interactive ToxPi graphical user interface (GUI) application has been implemented to allow straightforward decision support across a variety of decision-making contexts in environmental health. The GUI allows users to easily import and recombine data, then analyze, visualize, highlight, export and communicate ToxPi results. It also provides a statistical metric of stability for both individual ToxPi scores and relative prioritized ranks. The ToxPi GUI application, complete user manual and example data files are freely available from http://comptox.unc.edu/toxpi.php.

The development of medical image analysis algorithm is a complex process including the multiple sub-steps of model training, data visualization, human–computer interaction and graphical user interface (GUI) construction. To accelerate the development process, algorithm developers need a software tool to assist with all the sub-steps so that they can focus on the core function implementation. Especially, for the development of deep learning (DL) algorithms, a software tool supporting training data annotation and GUI construction is highly desired. In this work, we constructed AnatomySketch, an extensible open-source software platform with a friendly GUI and a flexible plugin interface for integrating user-developed algorithm modules. Through the plugin interface, algorithm developers can quickly create a GUI-based software prototype for clinical validation. AnatomySketch supports image annotation using the stylus and multi-touch screen. It also provides efficient tools to facilitate the collaboration between human experts and artificial intelligent (AI) algorithms. We demonstrate four exemplar applications including customized MRI image diagnosis, interactive lung lobe segmentation, human-AI collaborated spine disc segmentation and Annotation-by-iterative-Deep-Learning (AID) for DL model training. Using AnatomySketch, the gap between laboratory prototyping and clinical testing is bridged and the development of MIA algorithms is accelerated. The software is opened at https://github.com/DlutMedimgGroup/AnatomySketch-Software.

This is a unit test report for the Nuclear Facility Accident (NFAC) model for the Hazard Prediction and Assessment Capability (HPAC) version 6.3. NFAC’s responsibility as an HPAC component is three-fold. First, it must present an interactive graphical user interface (GUI) by which users can view and edit the definition of an NFAC incident. Second, for each incident defined, NFAC must interact with RTH to create activity table inputs and associate them with pseudo materials to be transported via SCIPUFF. Third, NFAC must create SCIPUFF releases with the associated pseudo materials for transport and dispersion. The goal of NFAC unit testing is to verify that the inputs it produces are correct for the source term or model definition as specified by the user via the GUI.

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

This paper presents an interactive design project of a remote clinical robot for ophthalmology, for use in support of telemedicine. The project is a case study of finding the best organizational structure of a Graphical User Interface (GUI) and Solid User Interface (SUI) in the age of ubiquitous computing. At the early stage of design, we carried out usability tests with paper-based prototypes and obtained requirements from ophthalmologists. Results showed that paper prototyping is applicable for GUI- and SUI-combined products, even they have manual control input and continuously changing visual feedback. We examine lessons learned from the design process and designed product.Keywordsdesign methodGraphical User Interface (GUI)interaction designophthalmologypaper prototypingremote clinical robotSolid User Interface (SUI)telemedicine