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A recent study conducted by researchers from the Icahn School of Medicine at Mount Sinai in New York City explores the use of advanced deep learning algorithms for magnetic resonance imaging (MRI) segmentation. Accurate MRI segmentation is crucial for diagnosing abnormalities and planning treatments, but traditional supervised learning techniques require a large amount of annotated data. The study reviews state-of-the-art algorithms that utilize a limited number of annotated samples, including self-supervised learning, generative models, few-shot learning, and semi-supervised learning. The researchers also discuss future directions in the field, such as contrastive language-image pretraining, to further improve image segmentation with limited labels. [Extracted from the article]

Researchers from Linkoping University in Sweden have developed an enhanced chameleon swarm algorithm (ICSA) for global optimization and multi-level thresholding medical image segmentation. Medical image segmentation is crucial for disease diagnosis and analysis, but selecting optimal thresholds can be computationally intensive. The ICSA algorithm improves precision and efficiency by introducing the concept of the "best random mutation strategy" and utilizing three distribution functions for mutating search individuals. Comparative analysis shows that ICSA outperforms other metaheuristic algorithms and significantly improves image segmentation performance. This research has been peer-reviewed and published in Neural Computing and Applications. [Extracted from the article]

Researchers at Chitkara University in Punjab, India have developed an improved lung cancer segmentation technique using nature-inspired optimization approaches. The study focused on the precise identification of tumor nodules in lung cancer Computed Tomography (CT) images. The researchers integrated four nature-inspired swarm intelligent techniques with the K-means algorithm to address the visual challenges of segmentation. The results showed that the K-means algorithm with Cuckoo Search Algorithm (CSA) significantly improved the quality of lung cancer segmentation compared to other optimization approaches. This research provides a valuable tool for researchers to improve the segmentation of medical image datasets for identifying the targeted region of interest. [Extracted from the article]

A recent study conducted by researchers at the First Affiliated Hospital of Wenzhou Medical University in China explores the use of deep learning algorithms in medical image analysis. The study highlights the importance of employing advanced deep learning techniques in this field, as they have shown impressive results in various areas of healthcare. The researchers categorized and assessed different deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), based on their principles, advantages, limitations, methodologies, and datasets. The study also addresses the challenges that hinder the widespread implementation of deep learning in medical image analysis. The findings of this research provide valuable insights for future studies and advancements in image analysis in medical healthcare research. [Extracted from the article]

A new imaging method for biological samples has been developed by IOCB Prague and the University of Munster. This method uses software to quickly and accurately identify chemicals in tissues, allowing researchers to visualize their distribution in organs. The software, called SIMSEF, was developed in collaboration with medical experts and can accelerate the detection of clinical biomarkers for diagnostic purposes. It is part of the open-source software package MZmine, which has been used by experts worldwide since 2005. The new algorithm enhances the capabilities of MZmine and allows for the linking of different types of data, including analytical and imaging methods. [Extracted from the article]

Researchers from Deenbandhu Chhotu Ram University of Science and Technology in India have published a new study on information technology management. The study focuses on the development of a brain-computer interface using a genetic algorithm with modified genome and phenotype structures. The researchers propose a novel scheme that combines genetic algorithms, support vector machines, and wavelet packet transform for motor imagery classification and optimal channel selection. The scheme outperforms existing methods in terms of classification accuracy and number of channels. This research contributes to the advancement of human-machine interface technology. [Extracted from the article]

A recent study conducted by researchers from Yanshan University in China explores the issue of inaccuracy in inhomogeneous sound speed fields in photoacoustic imaging (PAI), which can result in blurred and distorted images. The researchers propose a data-driven speed field inversion method that combines clustering with updates to the speed field. By dividing the model according to tissue similarity and adjusting the step length of sound speeds, the method improves the accuracy of the sound speed field and enhances the imaging performance of PAI. The study provides valuable insights into improving the quality of PAI without the need for additional information or equipment. [Extracted from the article]

A recent study conducted by researchers at Tianjin University in China has developed a blind watermarking scheme for copyright protection of medical and non-medical images. The scheme utilizes the QZ algorithm to embed the watermark into the host image, rather than using an addition-based process. The researchers found that their scheme was robust against various image-processing attacks and forgery attacks, while maintaining good invisibility. The study concluded that the proposed scheme outperformed existing addition-based schemes in terms of robustness. This research has been peer-reviewed and published in Expert Systems with Applications. [Extracted from the article]

A recent study conducted by researchers at Manipal Academy of Higher Education in India has developed a new approach to enhance precision in medical imaging. The study focuses on the detection of fiducial markers in neuroimaging, which are crucial reference points for accurate spatial alignment during image registration. The researchers propose a model that addresses challenges in marker placement and variability during MRI scanning, ensuring reliable localization for subsequent analysis. The model utilizes a 3D Convolutional Neural Network (CNN) and achieved exceptional accuracy and sensitivity in detecting fiducial points. The study concludes that this algorithm has the potential to improve accuracy and efficiency in neuroimaging studies. [Extracted from the article]

A research report from the University of Science and Technology of China discusses the use of dual-frequency ultrasounds in tumor treatment. The researchers propose a novel dual-frequency impedance matching network design using a genetic algorithm to compute component values. The implementation of this design resulted in improved transmission efficiency and expanded ablation zones in thermal ablation experiments. The researchers conclude that this dual-frequency impedance matching method holds promising prospects for application in dual-frequency ultrasound treatment. [Extracted from the article]

A recent report from the Alma Mater Studiorum - University of Bologna discusses the need for a simplified and cost-effective diagnostic algorithm for the surveillance of intraductal papillary mucinous neoplasms (IPMNs). The increased detection of pancreatic cysts has led to extensive diagnostic investigations, resulting in many patients undergoing numerous imaging follow-up studies for years. The researchers propose the use of unenhanced abbreviated-MRI (A-MRI) protocols as a surveillance tool for patients with IPMN, as it has shown high diagnostic performance in malignant detection. The aim of the study is to determine if implementing an A-MRI protocol could improve healthcare economics and reduce waiting lists without significantly reducing diagnostic accuracy. [Extracted from the article]

Keywords: Algorithms; Fuzzy Logic; Health and Medicine; Healthcare Engineering EN Algorithms Fuzzy Logic Health and Medicine Healthcare Engineering 2023 MAR 10 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Fresh data on healthcare engineering are presented in a new report. In this paper, we propose a novel brain MRI image segmentation algorithm based on fuzzy C-means (FCM) clustering algorithm to improve the segmentation accuracy. [Extracted from the article]

With the advancement of SIM, researchers have proposed a range of algorithms such as Open-SIM, fairSIM, Hessian-SIM, HiFi-SIM, etc. Keywords: Algorithms; Peking University; Software; Technology EN Algorithms Peking University Software Technology 159 159 1 08/07/23 20230812 NES 230812 2023 AUG 12 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Structured illumination microscopy (SIM) has emerged as the most powerful super-resolution technique for live cell imaging, owing to its inherent advantages of high speed, low phototoxicity, and compatibility with various dyes. [Extracted from the article]

Keywords: Algorithms; Cyborgs; DOE/Los Alamos National Laboratory; Emerging Technologies; Machine Learning EN Algorithms Cyborgs DOE/Los Alamos National Laboratory Emerging Technologies Machine Learning 2023 FEB 25 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- A team of scientists at Los Alamos National Laboratory is applying machine-learning algorithms to subsurface imaging that will impact a variety of applications, including energy exploration, carbon capture and sequestration and estimating pathways of subsurface contaminant transport, according to new research published in IEEE Signal Processing Magazine. Keywords for this news article include: Cyborgs, Algorithms, Machine Learning, Emerging Technologies, DOE/Los Alamos National Laboratory. [Extracted from the article]

A recent study from Liaoning University explores the application of machine learning in medicine. The researchers highlight the increasing quantity and complexity of medical data, such as medical images and genomics data, and the limitations of traditional analysis methods. Machine learning algorithms offer new opportunities for medical research and clinical practice by learning patterns and prediction models from large-scale data. The study examines the use of machine learning in medical image diagnosis, genomics, and drug discovery, emphasizing its potential to process complex medical data and support personalized medical care. [Extracted from the article]

A new report from Guilin University of Electronic Technology in China discusses the use of electrical impedance tomography (EIT) as a non-invasive imaging method. EIT allows for the acquisition of resistivity distribution information within an object without the use of radiation. The researchers propose a new algorithm based on a graph convolutional neural network model to improve the image reconstruction process in EIT. The experimental results demonstrate the effectiveness of the proposed algorithm across different domain shapes, grids, and non-distributed data. [Extracted from the article]

Researchers from Shahid Beheshti University of Medical Sciences in Tehran, Iran have developed a deep learning algorithm to enhance the quality of Magnetic Resonance Imaging (MRI) images and generate synthetic Computed Tomography (CT) images for more accurate radiotherapy planning. The study utilized the pix2pix Generative Adversarial Network to synthesize CT images from noisy MRI images of 20 patients with brain disease. The statistical measurements of the synthetic CT images were compared to referenced CT images, and the results showed that the proposed method provides an acceptable level of accuracy for radiotherapy treatment planning. [Extracted from the article]

A study conducted by Soonchunhyang University explores the use of an ensemble convolutional neural network (CNN) model with the line segment feature analysis (LFA) algorithm to classify the stage of Alzheimer's disease (AD) based on the shape of a patient's brain. The researchers combined a convolutional layer from the visual geometry group network (VGGNet) model with a 1D CNN model to improve the accuracy of AD classification. The proposed model demonstrated superior performance compared to existing methods, achieving an accuracy of 0.986 and a loss of 0.0385. This research highlights the importance of early diagnosis for the treatment and management of AD. [Extracted from the article]

A new report from researchers at the Department of Management in Dehradun, India, discusses the use of computer-aided detection (CAD) in classifying brain diseases, with a focus on Alzheimer's Disease (AD) and brain tumors. The study highlights the importance of expertise in interpreting MRI, PET, and CT scans for accurate diagnosis. The researchers propose pre-processing methods that improve the classification performance of MRI images and reduce training time for learning algorithms. The study concludes that their proposed algorithms outperformed existing methods in terms of accuracy and sensitivity. For more information, the full article can be accessed through the provided link. [Extracted from the article]

A preprint abstract from biorxiv.org discusses the development of a fast and efficient mixed-effects algorithm (FEMA) for analyzing large-scale neuroimaging datasets. The algorithm allows for whole-brain vertex-wise, voxel-wise, and connectome-wide linear mixed-effects model (LME) analyses in large samples. The researchers validate FEMA through extensive simulations and demonstrate its applicability by studying the effects of age on cortical thickness and functional connectivity using longitudinal imaging data. The findings reveal distinct spatial patterns in brain maturation during early adolescence. The FEMA algorithm enables advanced investigation of relationships between neuroimaging metrics and variables of interest in a fast and efficient manner. The source code for FEMA is available for access. [Extracted from the article]

Researchers from Ahvaz Jundishapur University of Medical Sciences have conducted a systematic review and meta-analysis to evaluate the use of machine learning (ML) algorithms in diagnosing intracranial hemorrhage (ICH) on computed tomography (CT) scans. The study found that ML algorithms have acceptable performance in diagnosing ICH, with a pooled sensitivity of 0.917 and a pooled specificity of 0.945. The ResNet algorithm showed higher specificity compared to other algorithms. The researchers suggest that using ResNet in ICH detection holds promise for improved prediction through training in an Architecture Learning Network (ALN). [Extracted from the article]

Researchers from National Taiwan University have developed an algorithm called YOLOv8 that can detect fractures in pediatric wrist trauma X-ray images. The algorithm uses data augmentation to improve its performance and has achieved a state-of-the-art mean average precision (mAP 50) of 0.638, which is higher than previous versions of the algorithm. The researchers have also designed an application called "Fracture Detection Using YOLOv8 App" to assist surgeons in diagnosing fractures and reducing the probability of error analysis. This research provides a valuable tool for surgeons in the field of pediatric wrist trauma. [Extracted from the article]

Algorithms, Diagnostics and Screening, Electroencephalography, Health and Medicine, Neuroscience, Human Neuroscience Keywords: Algorithms; Diagnostics and Screening; Electroencephalography; Health and Medicine; Human Neuroscience; Neuroscience EN Algorithms Diagnostics and Screening Electroencephalography Health and Medicine Human Neuroscience Neuroscience 428 428 1 05/22/23 20230526 NES 230526 2023 MAY 27 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- New study results on human neuroscience have been published. [Extracted from the article]

A recent study conducted by researchers at Sejong University in Seoul, South Korea, explored the use of machine learning algorithms and spatial criteria to model and map driver drowsiness. The study found that four machine learning algorithms, including bagged decision trees and random forest, had high accuracy in estimating drowsiness levels based on electroencephalogram (EEG) signals and spatial factors. The researchers concluded that mapping driver drowsiness using machine learning and spatial criteria can help identify high-risk areas and improve road safety. The study was published in Transportation Research Part F Traffic Psychology and Behaviour. [Extracted from the article]

Keywords: Patna; India; Asia; Mathematics; Algorithms; Genetic Algorithms; Genetics EN Patna India Asia Mathematics Algorithms Genetic Algorithms Genetics 501 501 1 10/16/23 20231020 NES 231020 2023 OCT 21 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- A new study on Mathematics is now available. Keywords for this news article include: Patna, India, Asia, Mathematics, Algorithms, Genetic Algorithms, Genetics, National Institute of Technology Patna. [Extracted from the article]

Keywords: Algorithms; Technology EN Algorithms Technology 5444 5444 1 10/09/23 20231013 NES 231013 2023 OCT 13 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Researchers detail new data in technology. Our news reporters obtained a quote from the research from University of Sargodha: "Kidney stones are a regular but life-threatening disease as they mostly remain unrecognized at the initial stages, leading to an increased threat of end-stage kidney failure. [Extracted from the article]

Keywords: Algorithms; Artificial Intelligence; Cancer; Diagnostics and Screening; Emerging Technologies; Health and Medicine; Machine Learning; Melanoma; Oncology EN Algorithms Artificial Intelligence Cancer Diagnostics and Screening Emerging Technologies Health and Medicine Machine Learning Melanoma Oncology 1564 1564 1 10/09/23 20231013 NES 231013 2023 OCT 13 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Investigators publish new report on artificial intelligence. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. [Extracted from the article]

Algorithms, Convolutional Network, Emerging Technologies, Health and Medicine, Machine Learning, Medical Imaging, Networks, Neural Networks Keywords: Algorithms; Convolutional Network; Emerging Technologies; Health and Medicine; Machine Learning; Medical Imaging; Networks; Neural Networks EN Algorithms Convolutional Network Emerging Technologies Health and Medicine Machine Learning Medical Imaging Networks Neural Networks 4749 4749 1 10/03/23 20231003 NES 231003 2023 OCT 6 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Investigators publish new report on medical imaging. [Extracted from the article]

Keywords: Algorithms; Genetic Algorithms; Genetics; Health and Medicine; Personalized Medicine EN Algorithms Genetic Algorithms Genetics Health and Medicine Personalized Medicine 7273 7273 1 09/11/23 20230911 NES 230911 2023 SEP 15 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- A new study on personalized medicine is now available. Algorithms, Genetic Algorithms, Genetics, Health and Medicine, Personalized Medicine. [Extracted from the article]

Keywords: Algorithms; Life Sciences; Ultrasound EN Algorithms Life Sciences Ultrasound 334 334 1 09/11/23 20230911 NES 230911 2023 SEP 16 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Current study results on life sciences have been published. According to the news reporters, the research concluded: "DL techniques show great potential in terms of US images acquired in preclinical studies using animal models. [Extracted from the article]

The news correspondents obtained a quote from the research from Hainan University: "In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. Algorithms, Cybersecurity, Electronics, Engineering, Legal Issues, Mobile Network, Networks, Technology Keywords: Algorithms; Cybersecurity; Electronics; Engineering; Legal Issues; Mobile Network; Networks; Technology EN Algorithms Cybersecurity Electronics Engineering Legal Issues Mobile Network Networks Technology 713 713 1 09/11/23 20230911 NES 230911 2023 SEP 16 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Researchers detail new data in electronics. [Extracted from the article]

Algorithms, Bioengineering, Biomedical Engineering, Biomedicine, Biotechnology, Computers, Health and Medicine, Regenerative Medicine Keywords: Algorithms; Bioengineering; Biomedical Engineering; Biomedicine; Biotechnology; Computers; Health and Medicine; Regenerative Medicine EN Algorithms Bioengineering Biomedical Engineering Biomedicine Biotechnology Computers Health and Medicine Regenerative Medicine 237 237 1 09/11/23 20230911 NES 230911 2023 SEP 14 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Investigators publish new report on regenerative medicine. Keywords for this news article include: Liaoning Petrochemical University, Liaoning, People's Republic of China, Asia, Biotechnology, Biomedical Engineering, Biomedicine, Computers, Algorithms, Bioengineering, Health and Medicine, Regenerative Medicine. [Extracted from the article]

Medical imaging techniques such as MRI and DaTScan can help diagnose PD by showing the changes in the brain structures related to the disease, such as the substantia nigra and the striatum." Keywords: Shaanxi; People's Republic of China; Asia; Algorithms; Basal Ganglia Diseases and Conditions; Brain Diseases and Conditions; Central Nervous System Diseases and Conditions; Diagnostics and Screening; Health and Medicine; Movement Disorders; Nervous System Diseases and Conditions; Neurodegenerative Diseases and Conditions; Parkinson's Disease; Parkinsonian Disorders; Software EN Shaanxi People's Republic of China Asia Algorithms Basal Ganglia Diseases and Conditions Brain Diseases and Conditions Central Nervous System Diseases and Conditions Diagnostics and Screening Health and Medicine Movement Disorders Nervous System Diseases and Conditions Neurodegenerative Diseases and Conditions Parkinson's Disease Parkinsonian Disorders Software 653 653 1 09/04/23 20230908 NES 230908 2023 SEP 9 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Current study results on Neurodegenerative Diseases and Conditions - Parkinson's Disease have been published. [Extracted from the article]

"The image segmentation algorithm could for example be a template-based image segmentation algorithm, an anatomical atlas-based image segmentation algorithm, a deformable shape image segmentation or other model-based segmentation, a neural network-based image segmentation algorithm, or other algorithm. Execution of the machine-executable instructions further causes the computational system to receive the image segmentation comprising the one or more anatomical regions within the magnetic resonance imaging data in response to inputting the initial magnetic resonance imaging data into the image segmentation algorithm. In another embodiment the initial magnetic resonance imaging data is dynamic magnetic resonance imaging data that comprises a time series of magnetic resonance images. "To enable the intensity corrected magnetic resonance imaging data to be useful in reconstructing the dynamic magnetic resonance image, the selected image portion is treated uniformly for each of the time series of the magnetic resonance images. [Extracted from the article]

Keywords: Algorithms; Angiography; Angiology; Cardiology; Cardiovascular Diagnostic Techniques; Coronary Artery; Genetic Algorithms; Genetics; Health and Medicine EN Algorithms Angiography Angiology Cardiology Cardiovascular Diagnostic Techniques Coronary Artery Genetic Algorithms Genetics Health and Medicine 990 990 1 07/03/23 20230707 NES 230707 2023 JUL 3 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Investigators publish new report on algorithms. Keywords for this news article include: Tanta University, Angiology, Cardiology, Angiography, Coronary Artery, Genetic Algorithms, Cardiovascular Diagnostic Techniques. [Extracted from the article]

Keywords: Algorithms; Diagnostics and Screening; Electroencephalography; Health and Medicine; Sensor Research; Technology EN Algorithms Diagnostics and Screening Electroencephalography Health and Medicine Sensor Research Technology 7638 7638 1 06/12/23 20230617 NES 230617 2023 JUN 16 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Investigators discuss new findings in sensor research. Algorithms, Diagnostics and Screening, Electroencephalography, Health and Medicine, Sensor Research, Technology. [Extracted from the article]

LESSONS: This is the first report of a glioma diagnosed by an AI algorithm and a subsequent prompt operation in the literature-the first of many and an example of how AI will enhance clinical practice." Keywords: Algorithms; Artificial Intelligence; Emerging Technologies; Gliomas; Health and Medicine; Machine Learning; Neurosurgery; Oncology; Surgery EN Algorithms Artificial Intelligence Emerging Technologies Gliomas Health and Medicine Machine Learning Neurosurgery Oncology Surgery 583 583 1 06/12/23 20230617 NES 230617 2023 JUN 12 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Research findings on artificial intelligence are discussed in a new report. [Extracted from the article]

Keywords: Algorithms; Health and Medicine; Neurosurgery; Sensor Research; Technology EN Algorithms Health and Medicine Neurosurgery Sensor Research Technology 4956 4956 1 06/05/23 20230609 NES 230609 2023 JUN 9 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- Investigators discuss new findings in sensor research. The proposed image-to-patient registration is implemented with the precise 3D registration of the extracted partial 3D CT data and the patient's scan data using the ICP algorithm." The 3D surface data of the patient are registered to CT data using computer-based optimization methods such as iterative closest point (ICP) algorithms. [Extracted from the article]

Keywords: Algorithms; Artificial Intelligence; Cancer; Diagnostics and Screening; Emerging Technologies; Health and Medicine; Machine Learning; Oncology; Thyroid Cancer; Thyroid Neoplasms EN Algorithms Artificial Intelligence Cancer Diagnostics and Screening Emerging Technologies Health and Medicine Machine Learning Oncology Thyroid Cancer Thyroid Neoplasms 1131 1131 1 05/29/23 20230602 NES 230602 2023 MAY 30 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- New research on artificial intelligence is the subject of a new report. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. [Extracted from the article]