Biomedical Imaging - Science topic

paper introduces a mathematical model employing swarm optimization, inspired by collective behaviour in nature, to tackle the complexities of multimodal biomedical image analysis. The Multimodal biomedical representation refers to the integration of information from multiple sources or modalities in the field of biomedical research or healthcare in...

Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemented with predictive and prognostic biomarker information is the first step for personalized treatment. Histopathological image (HI) analysis is the benchmark for pathologists to rank colorectal cancer o...

Medical imaging is one of the most efficient tools for visualizing the interior organs of the body and its associated diseases. Medical imaging is used to diagnose diseases and offer treatment. Since the manual examination of a massive number of Medical Images (MI) is a laborious and erroneous task, automated MI analysis approaches have been develo...

Large language models (LLMs) have demonstrated immense capabilities in understanding textual data and are increasingly being adopted to help researchers accelerate scientific discovery through knowledge extraction (information retrieval), knowledge distillation (summarizing key findings and methodologies into concise forms), and knowledge synthesis...

Doctors can effectively manage patients’ treatments and diseases by leveraging advanced medical imaging, which significantly minimizes guesswork and enhances diagnoses and treatments.The use of Deep Learning (DL) has been increasing recently in the area of medical imaging for various diseases like Parkinson’s, Alzheimer’s, Blood Cancer etc. When it...

Advances in bioimaging technologies capable of visualizing physiological and pathological processes are of great significance for revealing diseases development and promoting clinical diagnosis and treatment. Photoacoustic (PA) imaging, utilizing small‐molecule probes, offers a promising approach for achieving high‐spatiotemporal‐imaging of dynamic...

With the advent of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as liver, retina vessel, Nuclei and COVID-19 lesion segmentation, etc. Even though, the accuracy and the interpretablility of these methods still need to be futher improved. In this paper, we propose a novel Shape-aw...

In this text we propose a collection of enhanced quantum methods that would drastically enhance the efficiency and accuracy of biomedical image processing. There are four key methodologies that have been proposed in this work: Quantum Support Vector Machines with Enhanced Feature Extraction, Quantum Generative Adversarial Networks for Data Augmenta...

Este volume contém os artigos apresentados no SABIO 2024, VIII Simpósio de Inovação em Engenharia Biomédica, que aconteceu nos dias 20 a 22 de novembro de 2024. O evento teve como público alvo estudantes de graduação e pós-graduação, profissionais de Engenharia Biomédica, Ciências Médicas, Ciências da Saúde e áreas correlatas à tecnologia em saúde,...

Medical researchers and clinicians often need to perform novel segmentation tasks on a set of related images. Existing methods for segmenting a new dataset are either interactive, requiring substantial human effort for each image, or require an existing set of manually labeled images. We introduce a system, MultiverSeg, that enables practitioners t...

We propose a novel two-stage semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropagation. Rather, it exploits the bio-inspired Hebbian principle "fire together, wire together" as a local learning rule for updating the weights of both convolutional and trans...

METHODS RESULTS Participants: Eight patients with neuromuscular fatigue (M = 38 years, SD = 5 years, 4 female, 4 male) participated in this study. Study Design: Demographic and clinical data, degree of disability, and Diffusion Tensor Imaging (DTI) brain scans were collected before and after an 8-month intervention. Intervention: Weekly sessions fo...

The past decades have witnessed the significant development and practical interest of in vivo biomedical imaging technologies and optical materials in the second‐near infrared (NIR‐II, 1000–1700 nm) window. Imaging with the extended emission wavelength toward the long‐wavelength end (NIR‐IIb, 1500–1700 nm) further offers micrometer imaging resoluti...

Background Even though immune checkpoint inhibitors have improved survival rates for patients with recurrent and/or metastatic head and neck squamous cell carcinoma (HNSCC), a significant number of patients do not show durable responses. Previous studies have described correlation between overall survival and features derived from both collagen fib...

Background The tumor microenvironment (TME), embedded within the dense fibrous extracellular matrix (ECM) common to many solid tumors, significantly contributes to drug resistance and immunosuppression, thereby reducing the effectiveness of antitumor agents. Increasing the therapeutic dosage in the systemic circulation often fails to improve effica...

Biomedical image analysis plays a critical role in the healthcare system [...]

Medical image machines serve as a valuable tool to monitor and diagnose a variety of diseases. However, manual and centralized interpretation are both error-prone and time-consuming due to malicious attacks. Numerous diagnostic algorithms have been developed to improve precision and prevent poisoning attacks by integrating symptoms, test methods, a...

Following the successful publication of the first edition of our Special Issue entitled “Application of Nanomaterials in Biomedical Imaging and Cancer Therapy” [...]

In the biomedical field, photonic chip technology is gradually becoming the key to improving the speed and accuracy of medical imaging. The development of this technology has given us new hope in early disease diagnosis and cell monitoring. Recent research has not only sorted out the hotspots and future trends of biomedical photonics, but also paid...

The evaluation of segmentation performance is a common task in biomedical image analysis, with its importance emphasized in the recently released metrics selection guidelines and computing frameworks. To quantitatively evaluate the alignment of two segmentations, researchers commonly resort to counting metrics, such as the Dice similarity coefficie...

In the field of medicine, the preservation of medical confidentiality stands as paramount, for it directly influences the secure treatment and dissemination of medical data. Safeguarding against falsification or unauthorized access to patients' medical data, including medical images, is crucial. Conventional methods of image encryption, alongside c...

Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT...

Patellar tendinopathy (PT) typically affects jumping‐sport athletes with functional impairments frequently observed. Alterations to the functional organization of corticomotor neurons within the motor cortex that project to working muscles are evident in some musculoskeletal conditions and linked to functional impairments. We aimed to determine if...

Any serious outbreak of a novel infectious disease requires rapid innovation in testing technologies that can efficiently and accurately screen and diagnose active infection on both the individual and population levels. Delays in diagnostic testing imperil containment of an outbreak and delay control of a pandemic. In response to the dearth of diag...

Biotechnology is one of the emerging fields that can add new and better application in a wide range of sectors like health care, service sector, agriculture, and processing industry to name some. This book will provide an excellent opportunity to focus on recent developments in the frontier areas of Biotechnology and establish new collaborations in...

With increasing neuroimaging modalities and data diversity, mapping brain regions to a standard atlas template has become a challenging problem. Machine learning in general and deep learning, in particular, have been providing robust solutions for several neuroimaging tasks, including brain image registration and segmentation. However, these method...

Background: Two coding alleles within the APOL1 gene, G1 and G2, found almost exclusively in individuals genetically similar to West African populations, contribute substantially to the pathogenesis of chronic kidney disease (CKD). The APOL gene cluster on chromosome 22 contains a total of six APOL genes that have arisen as a result of gene duplic...

This review explores the latest advancements in nanoporous materials and their applications in biomedical imaging and diagnostics. Nanoporous materials possess unique structural features, including high surface area, tunable pore size, and versatile surface chemistry, making them highly promising platforms for a range of biomedical applications. Th...

Background: In biomedical imaging research, experimental biologists generate vast amounts of data that require advanced computational analysis. Breakthroughs in experimental techniques, such as multiplex immunofluorescence tissue imaging, enable detailed proteomic analysis, but most biomedical researchers lack the programming and Artificial Intelli...

Inverse problems in biomedical image analysis represent a significant frontier in disease detection, leveraging computational methodologies and mathematical modelling to unravel complex data embedded within medical images. These problems include deducing the unknown properties of biological structures or tissues from the observed imaging data, pres...

The book discusses the image processing basics, and its segmentation techniques using MR images with a detailed overview of brain anatomy from a functional point of view. Moreover, the establishment of tumour boards across the region was discussed from the MDT platform

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from established methodologies by utilizing a frozen transformer block, extracted from pre-trained LLMs, as an innovative...

Biomedical image segmentation plays a vital role in diagnosis of diseases across various organs. Deep learning-based object detection methods are commonly used for such segmentation. There exists an extensive research in this topic. However, there is no standard review on this topic. Existing surveys often lack a standardized approach or focus on b...

The integration of biomedical imaging techniques with advanced data analytics is at the forefront of a transformative era in healthcare [...]

In this research, we developed a two-stage deep learning (DL) model using Vision Transformer (ViT) to detect COVID-19 and assess its severity from thoracic CT images. In the first stage, we utilized a pre-trained ViT model (ViT_B/32) and a custom CNN model to classify CT images as COVID-19 or non-COVID-19. The ViT model achieved superior performanc...

The Biomedical Imaging and Therapy facility of the Canadian Light Source comprises two beamlines, which together cover a wide X‐ray energy range from 13 keV up to 140 keV. The beamlines were designed with a focus on synchrotron applications in preclinical imaging and veterinary science as well as microbeam radiation therapy. While these remain a ma...

The development of imaging, diagnosis, prognosis and early detection of diseases has been greatly impacted by nanotechnology by enhancing already existing clinically applicable technologies. With the help of their capacity to alter nanoparticles for molecular‐level specificity, tissue‐specific diagnosis is made possible gratitude to the unique biop...

This review paper provides an overview of complexity-based analysis techniques in biomedical image analysis, examining their theoretical foundations, computational methodologies, and practical applications across various medical imaging modalities. Through a synthesis of relevant literature, we explore the utility of complexity-based metrics such a...

In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrices cannot preserve the multidimensional correlation of elements in higher-order datasets and this hig...

Biotechnology is one of the emerging fields that can add new and better application in a wide range of sectors like health care, service sector, agriculture, and processing industry to name some. This book will provide an excellent opportunity to focus on recent developments in the frontier areas of Biotechnology and establish new collaborations in...

Diffusion models have demonstrated their effectiveness across various generative tasks. However, when applied to medical image segmentation, these models encounter several challenges, including significant resource and time requirements. They also necessitate a multi-step reverse process and multiple samples to produce reliable predictions. To addr...

Space-travel system comes with a number of difficulties that endanger the astronauts' survival in the intensely radiative environment by adversely affecting their physiological functions such as muscle deterioration, bone loss, kidney stones, infection, genetic disorder, and cardiovascular adaptation. The maintenance of pharmaceutical stability is...

The increasing availability of biomedical data is helping to design more robust deep learning (DL) algorithms to analyze biomedical samples. Currently, one of the main limitations to train DL algorithms to perform a specific task is the need for medical experts to label data. Automatic methods to label data exist, however automatic labels can be no...

In the biomedical imaging domain, large preprocessed samples of training annotated images are required in techniques employing neural networks for effective training, which makes the method challenging and costly. Data augmentation is widely used to widen the pool of training samples by providing augmented data, enabling the learning method to capt...

3D data from high-resolution volumetric imaging is a central resource for diagnosis and treatment in modern medicine. While the fast development of AI enhances imaging and analysis, commonly used visualization methods lag far behind. Recent research used extended reality (XR) for perceiving 3D images with visual depth perception and touch but used...

Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different pr...

In recent years, with the widespread application of medical images, the rapid and accurate identification of these regions of interest in a large number of medical images has received widespread attention. This article provides a review of medical image segmentation methods based on deep learning. Firstly, an overview of medical image segmentation...

Generative Adversarial Networks (GANs) have high computational costs to train their complex architectures. Throughout the training process, GANs' output is analyzed qualitatively based on the loss and synthetic images' diversity and quality. Based on this qualitative analysis, training is manually halted once the desired synthetic images are genera...

A wide variety of biomedical image data, as well as methods for generating training images using basic deep neural networks, were analyzed. Additionally, all platforms for creating images were analyzed, considering their characteristics. The article develops a method for generating artificial biomedical images based on GAN. GAN architecture has bee...

Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhances the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional...

Biotechnology is one of the emerging fields that can add new and better application in a wide range of sectors like health care, service sector, agriculture, and processing industry to name some. This book will provide an excellent opportunity to focus on recent developments in the frontier areas of Biotechnology and establish new collaborations in...

A literature review reveals that, at the moment, all usability tests for Software as a Medical Device (SaMD) are designed in compliance with international standards but it also reveals a lack of formalization in the implementation and administration of such usability tests, which prevents the comparison of results from different tests for the same...

Fluorescence microscopy plays a crucial role in cellular analysis but is often hindered by phototoxicity and limited spectral channels. Label-free transmitted light microscopy presents an attractive alternative, yet recovering fluorescence images from such inputs remains difficult. In this work, we address the Cell Painting problem within the Light...

Over the past century, multichannel fluorescence imaging has been pivotal in myriad scientific breakthroughs by enabling the spatial visualization of proteins within a biological sample. With the shift to digital methods and visualization software, experts can now flexibly pseudocolor and combine image channels, each corresponding to a different pr...

“BAU-Insectv2” represents a novel agricultural dataset tailored for deep learning applications and biomedical image analysis focused on plant-insect interactions. This dataset encompasses a diverse collection of high-resolution images capturing intricate details of plant-insect interactions across various agricultural settings. Leveraging deep lear...

In the current realm of biomedical image classification, the predominant choice remains deep learning networks, particularly convolutional neural network (CNN) models. However, deep learning suffers from a notable drawback in terms of its high training cost, mainly due to intricate data models. A recent alternative, known as the Extreme Learning Ma...

The domain of real-time biomedical imaging has seen remarkable technological advances, enhancing the efficacy of surgical interventions. This paper addresses the critical challenges associated with the implementation of real-time biomedical imaging systems for surgical guidance and discusses comprehensive solutions to mitigate these issues. It outl...

Transient terahertz time-domain spectroscopy (THz-TDS) imaging has emerged as a novel non-ionizing and noninvasive biomedical imaging modality, designed for the detection and characterization of a variety of tissue malignancies due to their high signal-to-noise ratio and submillimeter resolution. We report our design of a pair of aspheric focusing...

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation i...

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical ima...

This study unveils an innovative method for synthesizing coumarin S-glycosides, employing original biocatalysts able to graft diverse carbohydrate structures onto 7-mercapto-4-methyl-coumarin in one-pot reactions. The fluorescence properties of the generated thio-derivatives were assessed, providing valuable insights into their potential applicatio...

Purpose Deformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high‐quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by...

The injectable hydrogels can deliver the loads directly to the predetermined sites and form reservoirs to increase the enrichment and retention of the loads in the target areas. The preparation and injection of injectable hydrogels involve the sol–gel transformation of hydrogels, which is affected by factors such as temperature, ions, enzymes, ligh...

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiol...

Biomedical imaging innovation facilitates a better understanding of the heart’s physiology, performance, function, and structure [...]