Augmented reality simulation framework for minimally invasive orthopedic surgery.

Purpose: Minimally invasive surgery (MIS) has emerged in clinical practice to minimize surgical trauma, providing patients with faster recovery, reduced pain and complications and enhanced aesthetic results compared to traditional surgery. However, this approach increase the risk of iatrogenic damage, i.e.

Enhancing fetal ultrasound image quality and anatomical plane recognition in low-resource settings using super-resolution models.

Super-resolution (SR) techniques present a suitable solution to increase the image resolution acquired using an ultrasound device characterized by a low image resolution. This can be particularly beneficial in low-resource imaging settings. This work surveys advanced SR techniques applied to enhance the resolution and quality of fetal ultrasound images, focusing Dual back-projection based internal learning (DBPISR) technique, which utilizes internal learning for blind super-resolution, as opposed to blind super-resolution generative adversarial network (BSRGAN), real-world enhanced super-resolution generative adversarial network (Real-ESRGAN), swin transformer for image restoration (SwinIR) and SwinIR-Large.

Ultrastructure of astrocytes using volume electron microscopy: A scoping review.

The morphological features of astrocytes are crucial for brain homeostasis, synaptic activity and structural support, yet remain poorly quantified. As a result of the nanometre-sized cross-section of neuropil astrocytic processes, electron microscopy (EM) is the only technique availabe to date capable of revealing their finest morphologies. Volume EM (vEM) techniques, such as serial block-face or focused ion beam scanning EM, enable high-resolution imaging of large fields and allow more extensive 3-D model analyses, revealing new astrocytic morphological features.

Visualizing Mental Health Insights: A Pipeline from Social Media to Chernoff Faces.

This study proposes an approach for analyzing mental health through publicly available social media data, employing Large Language Models (LLMs) and visualization techniques to transform textual data into Chernoff Faces. The analysis began with a dataset comprising 15,744 posts sourced from major social media platforms, which was refined down to 2,621 posts through meticulous data cleaning, feature extraction, and visualization processes. Our methodology includes stages of Data Preparation, Feature Extraction, Chernoff Face Visualization, and Clinical Validation.

Unveiling the Potential of ChatGPT and YOLOv7 for Evaluating Children's Emotions Using Their Artistic Expressions.

Recent advancements in large language models (LLMs) have sparked considerable interest in their potential applications across various healthcare domains. One promising prospect is leveraging these generative models to accurately predict children's emotions by combining computer vision and natural language processing techniques. However, understanding children's emotional states based on their artistic expressions is equally crucial.

FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery.

FetSAM represents a cutting-edge deep learning model aimed at revolutionizing fetal head ultrasound segmentation, thereby elevating prenatal diagnostic precision. Utilizing a comprehensive dataset-the largest to date for fetal head metrics-FetSAM incorporates prompt-based learning. It distinguishes itself with a dual loss mechanism, combining Weighted DiceLoss and Weighted Lovasz Loss, optimized through AdamW and underscored by class weight adjustments for better segmentation balance.

The metaverse digital environments: a scoping review of the challenges, privacy and security issues.

The concept of the "metaverse" has garnered significant attention recently, positioned as the "next frontier" of the internet. This emerging digital realm carries substantial economic and financial implications for both IT and non-IT industries. However, the integration and evolution of these virtual universes bring forth a multitude of intricate issues and quandaries that demand resolution.

Large-scale annotation dataset for fetal head biometry in ultrasound images.

This dataset features a collection of 3832 high-resolution ultrasound images, each with dimensions of 959×661 pixels, focused on Fetal heads. The images highlight specific anatomical regions: the brain, cavum septum pellucidum (CSP), and lateral ventricles (LV). The dataset was assembled under the Creative Commons Attribution 4.

Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey.

Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns.

Assessing the Landscape of Telehealth Interventions for Women's Health: An Umbrella Review of Recent Advances Post-Pandemic.

This umbrella review aims to provide a comprehensive overview of the use of telehealth services for women after the COVID-19 pandemic. The review synthesizes findings from 21 reviews, covering diverse topics such as cancer care, pregnancy and postpartum care, general health, and specific populations. While some areas have shown promising results, others require further research to better understand the potential of digital health interventions.

Deep Scene Synthesis of Atlanta-World Interiors from a Single Omnidirectional Image.

We present a new data-driven approach for extracting geometric and structural information from a single spherical panorama of an interior scene, and for using this information to render the scene from novel points of view, enhancing 3D immersion in VR applications. The approach copes with the inherent ambiguities of single-image geometry estimation and novel view synthesis by focusing on the very common case of Atlanta-world interiors, bounded by horizontal floors and ceilings and vertical walls. Based on this prior, we introduce a novel end-to-end deep learning approach to jointly estimate the depth and the underlying room structure of the scene.

Telehealth interventions during COVID-19 pandemic: a scoping review of applications, challenges, privacy and security issues.

Background: The COVID-19, caused by the SARS-CoV-2 virus, proliferated worldwide, leading to a pandemic. Many governmental and non-governmental organisations and research institutes are contributing to the COVID-19 fight to control the pandemic.

Motivation: Numerous telehealth applications have been proposed and adopted during the pandemic to combat the spread of the disease.

Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience.

Unlabelled: Ultraliser is a neuroscience-specific software framework capable of creating accurate and biologically realistic 3D models of complex neuroscientific structures at intracellular (e.g. mitochondria and endoplasmic reticula), cellular (e.

Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction.

Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images.

Instant Automatic Emptying of Panoramic Indoor Scenes.

Nowadays 360° cameras, capable to capture full environments in a single shot, are increasingly being used in a variety of Extended Reality (XR) applications that require specific Diminished Reality (DR) techniques to conceal selected classes of objects. In this work, we present a new data-driven approach that, from an input 360° image of a furnished indoor space automatically returns, with very low latency, an omnidirectional photorealistic view and architecturally plausible depth of the same scene emptied of all clutter. Contrary to recent data-driven inpainting methods that remove single user-defined objects based on their semantics, our approach is holistically applied to the entire scene, and is capable to separate the clutter from the architectural structure in a single step.

Toward deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via ultrasound images.

Several reviews have been conducted regarding artificial intelligence (AI) techniques to improve pregnancy outcomes. But they are not focusing on ultrasound images. This survey aims to explore how AI can assist with fetal growth monitoring via ultrasound image.

A Conceptual Framework for Fetus Head Analysis Based on Ultrasound Images.

Ultrasound images are the most used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. In particular, the obstetrician uses fetus head images to monitor the growth state and identify essential features such as Gestational age (GA), estimated fetus weight (EFW), and brain anatomical structures. However, this work requires an expert obstetrician, and it is time-consuming and costly.

Probabilistic Occlusion Culling using Confidence Maps for High-Quality Rendering of Large Particle Data.

Achieving high rendering quality in the visualization of large particle data, for example from large-scale molecular dynamics simulations, requires a significant amount of sub-pixel super-sampling, due to very high numbers of particles per pixel. Although it is impossible to super-sample all particles of large-scale data at interactive rates, efficient occlusion culling can decouple the overall data size from a high effective sampling rate of visible particles. However, while the latter is essential for domain scientists to be able to see important data features, performing occlusion culling by sampling or sorting the data is usually slow or error-prone due to visibility estimates of insufficient quality.

Augmented Reality in Medical Practice: From Spine Surgery to Remote Assistance.

While performing surgeries in the OR, surgeons and assistants often need to access several information regarding surgical planning and/or procedures related to the surgery itself, or the accessory equipment to perform certain operations. The accessibility of this information often relies on the physical presence of technical and medical specialists in the OR, which is increasingly difficult due to the number of limitations imposed by the COVID emergency to avoid overcrowded environments or external personnel. Here, we analyze several scenarios where we equipped OR personnel with augmented reality (AR) glasses, allowing a remote specialist to guide OR operations through voice and visuals, superimposed to the field of view of the operator wearing them.

The Mixture Graph-A Data Structure for Compressing, Rendering, and Querying Segmentation Histograms.

In this paper, we present a novel data structure, called the Mixture Graph. This data structure allows us to compress, render, and query segmentation histograms. Such histograms arise when building a mipmap of a volume containing segmentation IDs.

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells.

Serial sectioning and subsequent high-resolution imaging of biological tissue using electron microscopy (EM) allow for the segmentation and reconstruction of high-resolution imaged stacks to reveal ultrastructural patterns that could not be resolved using 2D images. Indeed, the latter might lead to a misinterpretation of morphologies, like in the case of mitochondria; the use of 3D models is, therefore, more and more common and applied to the formulation of morphology-based functional hypotheses. To date, the use of 3D models generated from light or electron image stacks makes qualitative, visual assessments, as well as quantification, more convenient to be performed directly in 3D.

3D cellular reconstruction of cortical glia and parenchymal morphometric analysis from Serial Block-Face Electron Microscopy of juvenile rat.

With the rapid evolution in the automation of serial electron microscopy in life sciences, the acquisition of terabyte-sized datasets is becoming increasingly common. High resolution serial block-face imaging (SBEM) of biological tissues offers the opportunity to segment and reconstruct nanoscale structures to reveal spatial features previously inaccessible with simple, single section, two-dimensional images. In particular, we focussed here on glial cells, whose reconstruction efforts in literature are still limited, compared to neurons.

A Process for Digitizing and Simulating Biologically Realistic Oligocellular Networks Demonstrated for the Neuro-Glio-Vascular Ensemble.

One will not understand the brain without an integrated exploration of structure and function, these attributes being two sides of the same coin: together they form the currency of biological computation. Accordingly, biologically realistic models require the re-creation of the architecture of the cellular components in which biochemical reactions are contained. We describe here a process of reconstructing a functional oligocellular assembly that is responsible for energy supply management in the brain and creating a computational model of the associated biochemical and biophysical processes.

Culling for Extreme-Scale Segmentation Volumes: A Hybrid Deterministic and Probabilistic Approach.

With the rapid increase in raw volume data sizes, such as terabyte-sized microscopy volumes, the corresponding segmentation label volumes have become extremely large as well. We focus on integer label data, whose efficient representation in memory, as well as fast random data access, pose an even greater challenge than the raw image data. Often, it is crucial to be able to rapidly identify which segments are located where, whether for empty space skipping for fast rendering, or for spatial proximity queries.

SparseLeap: Efficient Empty Space Skipping for Large-Scale Volume Rendering.

Recent advances in data acquisition produce volume data of very high resolution and large size, such as terabyte-sized microscopy volumes. These data often contain many fine and intricate structures, which pose huge challenges for volume rendering, and make it particularly important to efficiently skip empty space. This paper addresses two major challenges: (1) The complexity of large volumes containing fine structures often leads to highly fragmented space subdivisions that make empty regions hard to skip efficiently.