ColocZStats: a z-stack signal colocalization extension tool for 3D slicer.

Confocal microscopy has evolved to be a widely adopted imaging technique in molecular biology and is frequently utilized to achieve accurate subcellular localization of proteins. Applying colocalization analysis on image z-stacks obtained from confocal fluorescence microscopes is a dependable method of revealing the relationship between different molecules. In addition, despite the established advantages and growing adoption of 3D visualization software in various microscopy research domains, there have been few systems that can support colocalization analysis within a user-specified region of interest (ROI). In this context, several broadly employed biological image visualization platforms are meticulously explored in this study to understand the current landscape. It has been observed that while these applications can generate three-dimensional (3D) reconstructions for z-stacks, and in some cases transfer them into an immersive virtual reality (VR) scene, there is still little support for performing quantitative colocalization analysis on such images based on a user-defined ROI and thresholding levels. To address these issues, an extension called ColocZStats (pronounced Coloc-Zee-Stats) has been developed for 3D Slicer, a widely used free and open-source software package for image analysis and scientific visualization. With a custom-designed user-friendly interface, ColocZStats allows investigators to conduct intensity thresholding and ROI selection on imported 3D image stacks. It can deliver several essential colocalization metrics for structures of interest and produce reports in the form of diagrams and spreadsheets.

SmartVR Pointer: Using Smartphones and Gaze Orientation for Selection and Navigation in Virtual Reality.

Some of the barriers preventing virtual reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here, we propose that in many cases, the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones to replace the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer, a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker based, gaze based, and combined/hybrid. In the tracker-based SmartVR Pointer selection, we use image-based tracking to track a QR code displayed on the phone screen and then map the phone's position to a pointer shown within the field of view of the camera in the virtual environment. In the gaze-based selection modality, the user controls the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between gaze-based interaction in VR and tracker-based Augmented Reality. It allows the user to control a VR pointer that looks and behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone's touch screen. The touchscreen is used for selection and dragging. The SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge.

Use of Extended Reality in Medical Education: An Integrative Review.

Extended reality (XR) has emerged as an innovative simulation-based learning modality. An integrative review was undertaken to explore the nature of evidence, usage, and effectiveness of XR modalities in medical education. One hundred and thirty-three (N = 133) studies and articles were reviewed. XR technologies are commonly reported in surgical and anatomical education, and the evidence suggests XR may be as effective as traditional medical education teaching methods and, potentially, a more cost-effective means of curriculum delivery. Further research to compare different variations of XR technologies and best applications in medical education and training are required to advance the field. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01698-4.

Novel segmentation algorithm for high-throughput analysis of spectral domain-optical coherence tomography imaging of teleost retinas.

Spectral domain-optical coherence tomography (SD-OCT) has become an essential tool for assessing ocular tissues in live subjects and conducting research on ocular development, health, and disease. The processing of SD-OCT images, particularly those from non-mammalian species, is a labor-intensive manual process due to a lack of automated analytical programs. This paper describes the development and implementation of a novel computer algorithm for the quantitative analysis of SD-OCT images of live teleost eyes. Automated segmentation processing of SD-OCT images of retinal layers was developed using a novel algorithm based on thresholding. The algorithm measures retinal thickness characteristics in a large volume of imaging data of teleost ocular structures in a short time, providing increased accuracy and repeatability of SD-OCT image analysis over manual measurements. The algorithm also generates hundreds of retinal thickness measurements per image for a large number of images for a given dataset. Meanwhile, heat mapping software that plots SD-OCT image measurements as a color gradient was also created. This software directly converts the measurements of each processed image to represent changes in thickness across the whole retinal scan. It also enables 2D and 3D visualization of retinal thickness across the scan, facilitating specimen comparison and localization of areas of interest. The study findings showed that the novel algorithm is more accurate, reliable, and repeatable than manual SD-OCT analysis. The adaptability of the algorithm makes it potentially suitable for analyzing SD-OCT scans of other non-mammalian species.

Augmented Reality as a Telemedicine Platform for Remote Procedural Training.

Traditionally, rural areas in many countries are limited by a lack of access to health care due to the inherent challenges associated with recruitment and retention of healthcare professionals. Telemedicine, which uses communication technology to deliver medical services over distance, is an economical and potentially effective way to address this problem. In this research, we develop a new telepresence application using an Augmented Reality (AR) system. We explore the use of the Microsoft HoloLens to facilitate and enhance remote medical training. Intrinsic advantages of AR systems enable remote learners to perform complex medical procedures such as Point of Care Ultrasound (PoCUS) without visual interference. This research uses the HoloLens to capture the first-person view of a simulated rural emergency room (ER) through mixed reality capture (MRC) and serves as a novel telemedicine platform with remote pointing capabilities. The mentor's hand gestures are captured using a Leap Motion and virtually displayed in the AR space of the HoloLens. To explore the feasibility of the developed platform, twelve novice medical trainees were guided by a mentor through a simulated ultrasound exploration in a trauma scenario, as part of a pilot user study. The study explores the utility of the system from the trainees, mentor, and objective observers' perspectives and compares the findings to that of a more traditional multi-camera telemedicine solution. The results obtained provide valuable insight and guidance for the development of an AR-supported telemedicine platform.

GeNET: a web application to explore and share Gene Co-expression Network Analysis data.

Gene Co-expression Network Analysis (GCNA) is a popular approach to analyze a collection of gene expression profiles. GCNA yields an assignment of genes to gene co-expression modules, a list of gene sets statistically over-represented in these modules, and a gene-to-gene network. There are several computer programs for gene-to-gene network visualization, but these programs have limitations in terms of integrating all the data generated by a GCNA and making these data available online. To facilitate sharing and study of GCNA data, we developed GeNET. For researchers interested in sharing their GCNA data, GeNET provides a convenient interface to upload their data and automatically make it accessible to the public through an online server. For researchers interested in exploring GCNA data published by others, GeNET provides an intuitive online tool to interactively explore GCNA data by genes, gene sets or modules. In addition, GeNET allows users to download all or part of the published data for further computational analysis. To demonstrate the applicability of GeNET, we imported three published GCNA datasets, the largest of which consists of roughly 17,000 genes and 200 conditions. GeNET is available at bengi.cs.mun.ca/genet.

OMARC: An online multimedia application for training health care providers in the assessment of respiratory conditions.

OBJECTIVES: OMARC, a multimedia application designed to support the training of health care providers for the identification of common lung sounds heard in a patient's thorax as part of a health assessment, is described and its positive contribution to user learning is assessed. The main goal of OMARC is to effectively help health-care students become familiar with lung sounds as part of the assessment of respiratory conditions. In addition, the application must be easy to use and accessible to students and practitioners over the internet. SYSTEM DESCRIPTION: OMARC was developed using an online platform to facilitate access to users in remote locations. OMARC's unique contribution as an educational software tool is that it presents a narrative about normal and abnormal lung sounds using interactive multimedia and sample case studies designed by professional health-care providers and educators. Its interface consists of two distinct components: a sounds glossary and a rich multimedia interface which presents clinical case studies and provides access to lung sounds placed on a model of a human torso. OMARC's contents can be extended through the addition of sounds and case studies designed by health-care educators and professionals. VALIDATION AND RESULTS: To validate OMARC and determine its efficacy in improving learning and capture user perceptions about it, we performed a pilot study with ten nursing students. Participants' performance was measured through an evaluation of their ability to identify several normal and adventitious/abnormal sounds prior and after exposure to OMARC. Results indicate that participants are able to better identify different lung sounds, going from an average of 63% (S.D. 18.3%) in the pre-test evaluation to an average of 90% (S.D. of 11.5%) after practising with OMARC. Furthermore, participants indicated in a user satisfaction questionnaire that they found the application helpful, easy to use and that they would recommend it to other persons in their field. CONCLUSIONS: OMARC is an online multimedia application for training health care students in the assessment of respiratory conditions. The software integrates multimedia technology and health-care education concepts to facilitate learning, while being useful and easy to use. Results from a pilot study indicate that OMARC significantly helps to improve the capacity of the users to correctly identify lung sounds for different respiratory conditions. In addition, participants' opinions about OMARC were quite positive: users were likely to recommend the application to other persons in their field and found the application easy to use and helpful to better identify lung sounds.

Estimating cell count and distribution in labeled histological samples using incremental cell search.

Cell proliferation is critical to the outgrowth of biological structures including the face and limbs. This cellular process has traditionally been studied via sequential histological sampling of these tissues. The length and tedium of traditional sampling is a major impediment to analyzing the large datasets required to accurately model cellular processes. Computerized cell localization and quantification is critical for high-throughput morphometric analysis of developing embryonic tissues. We have developed the Incremental Cell Search (ICS), a novel software tool that expedites the analysis of relationships between morphological outgrowth and cell proliferation in embryonic tissues. Based on an estimated average cell size and stain color, ICS rapidly indicates the approximate location and amount of cells in histological images of labeled embryonic tissue and provides estimates of cell counts in regions with saturated fluorescence and blurred cell boundaries. This capacity opens the door to high-throughput 3D and 4D quantitative analyses of developmental patterns.

Integrative visualization of temporally varying medical image patterns.

We have developed a tool for the visualization of temporal changes of disease patterns, using stacks of medical images collected in time-series experiments. With this tool, users can generate 3D surface models representing disease patterns and observe changes over time in size, shape, and location of clinically significant image patterns. Statistical measurements of the volume of the observed disease patterns can be performed simultaneously. Spatial data integration occurs through the combination of 2D slices of an image stack into a 3D surface model. Temporal integration occurs through the sequential visualization of the 3D models from different time points. Visual integration enables the tool to show 2D images, 3D models and statistical data simultaneously. As an example, the tool has been used to visualize brain MRI scans of several multiple sclerosis patients. It has been developed in Java™, to ensure portability and platform independence, with a user-friendly interface and can be downloaded free of charge for academic users.

Generation of connectivity-preserving surface models of multiple sclerosis lesions.

Progression of multiple sclerosis (MS) results in brain lesions caused by white matter inflammation. MS lesions have various shapes, sizes and locations, affecting cognitive abilities of patients to different extents. To facilitate the visualization of the brain lesion distribution, we have developed a software tool to build 3D surface models of MS lesions. This tool allows users to create 3D models of lesions quickly and to visualize the lesions and brain tissues using various visual attributes and configurations. The software package is based on breadth-first search based 3D connected component analysis and a 3D flood-fill based region growing algorithm to generate 3D models from binary or non-binary segmented medical image stacks.

Building generic anatomical models using virtual model cutting and iterative registration.

BACKGROUND: Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limited views of them that medical images can offer, it is still an exceptionally difficult task to quickly and accurately create 3D generic models (a model is a 3D graphical representation of a biological structure) based on medical image stacks (a stack is an ordered collection of 2D images). We show that the creation of a generic model that captures spatial information exploitable in statistical analyses is facilitated by coupling our generalized segmentation method to existing automatic image registration algorithms. METHODS: The method of creating generic 3D models consists of the following processing steps: (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) interactively extracting sub-volume by cutting each model to generate the sub-model of interest; (iv) creating image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models. RESULTS: After several registration procedures are applied to the image stacks, we can create averaged image stacks with sharp boundaries. The averaged 3D model created from those image stacks is very close to the average representation of the population. The image registration time varies depending on the image size and the desired accuracy of the registration. Both volumetric data and surface model for the generic 3D model are created at the final step. CONCLUSIONS: Our method is very flexible and easy to use such that anyone can use image stacks to create models and retrieve a sub-region from it at their ease. Java-based implementation allows our method to be used on various visualization systems including personal computers, workstations, computers equipped with stereo displays, and even virtual reality rooms such as the CAVE Automated Virtual Environment. The technique allows biologists to build generic 3D models of their interest quickly and accurately.

Fast interactive integration of cross-sectional image datasets and surface data for morphometric analysis.

To investigate external facial morphology and cell proliferation patterns and their relationship with cleft lip malformation in mice, we need to compare samples of mice tissue photographs and surface reconstructions from micro-CT scans obtained from mouse embryos. Tissue samples obtained through digital photography are typically misaligned with respect to each other, which prevents further analysis. We have developed a system for fast interactive alignment of these image stacks for volume reconstruction and data visualization and analysis in 3D. The system is designed to work in multiprocessor environments and can utilize an arbitrary number of processors, cutting down significantly the turnaround time and allowing users to quickly process sets of hundreds of high resolution images using a combination of automated and interactive tools. Additional modules are used to reconstruct the shape of the original subject. Our system is interactive, fully scalable and can be applied to any photographic sliced dataset, regardless of subject and reduces significantly the processing time for stack alignment.

An efficient virtual dissection tool to create generic models for anatomical atlases.

We have developed an efficient virtual dissection tool to create generic 3D models for anatomical atlases without the need for artistic drawings. Our custom-developed tool can be used to extract 3D models from 2D medical image stacks, cut the models and align the sub-models. Corresponding 2D medical image portions of the sub-models can then be registered and averaged. In the end, a generic model can be obtained from the averaged 2D images of several subjects. The technique optimizes the functionalities of existing toolkits and the resulting software package will allow biologists to build their atlases more quickly and accurately.