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Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-09 Samuel Ruipérez-Campillo; Sergio Castrejón; Marcel Martínez; Raquel Cervigón; Olivier Meste; José Luis Merino; José Millet; Francisco Castells
Background and objectives Macroreentrant atrial tachyarrhythmias (MRATs) can be caused by different reentrant circuits. The treatment for each MRAT type may require ablation at different sites, either at the right or left atria. Unfortunately, the reentrant circuit that drives the arrhythmia cannot be ascertained previous to the electrophysiological intervention. Methods A noninvasive approach based
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Efficient network selection for computer-aided cataract diagnosis under noisy environment Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-09 Turimerla Pratap; Priyanka Kokil
Background and objective:Computer-aided cataract diagnosis (CACD) methods play a crucial role in early detection of cataract. The existing CACD methods are suffering from performance diminution due to the presence of noise in digital fundus retinal images. The lack of robustness in CACD methods against noise is a serious concern since even the presence of small noise levels may degrade the performance
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On construction of transfer learning for facial symmetry assessment before and after orthognathic surgery Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-09 Hsiu-Hsia Lin; Wen-Chung Chiang; Chao-Tung Yang; Chun-Tse Cheng; Tianyi Zhang; Lun-Jou Lo
Orthognathic surgery (OGS) is frequently used to correct facial deformities associated with skeletal malocclusion and facial asymmetry. An accurate evaluation of facial symmetry is a critical for precise surgical planning and the execution of OGS. However, no facial symmetry scoring standard is available. Typically, orthodontists or physicians simply judge facial symmetry. Therefore, maintaining accuracy
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An exploration of adolescent facial shape changes with age via multilevel partial least squares regression Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-08 D.J.J. Farnell; S. Richmond; J. Galloway; A.I. Zhurov; P. Pirttiniemi; T. Heikkinen; V. Harila; H. Matthews; P. Claes
Background and Objectives Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males
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A Robust Deep Learning-based Multiclass Segmentation Method for Analyzing Human Metaphase II Oocyte Images Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-19 Sedighe Firuzinia; Seyed Mahmoodreza Afzali; Fatemeh Ghasemian; Seyed Abolghasem Mirroshandel
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Faster region convolutional neural network and semen tracking algorithm for sperm analysis Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-09 Devaraj Somasundaram; Madian Nirmala
Background and objectives Semen analysis is a primary and mandatory procedure to evaluate the infertility during clinical examination. This procedure includes the analysis and classification of normal and abnormal Sperm, selection and efficient tracking of healthy sperm in the sample. Many methods were proposed earlier for the analysis of semen. The fast sperm movement and high dense cluster of sperm
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Comparison of machine learning models to classify Auditory Brainstem Responses recorded from children with Auditory Processing Disorder Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-17 Hasitha Wimalarathna; Sangamanatha Ankmnal-Veeranna; Chris Allan; Sumit Agrawal; Prudence Allen; Jagath Samarabandu; Hanif Ladak
Introduction : Auditory brainstem responses (ABRs) offer a unique opportunity to assess the neural integrity of the peripheral auditory nervous system in individuals presenting with listening difficulties. ABRs are typically recorded and analyzed by an audiologist who manually measures the timing and quality of the waveforms. The interpretation of ABRs requires considerable experience and training
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Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings. Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-17 Mohanad Alkhodari; Luay Fraiwan
Valvular heart diseases (VHD) are one of the major causes of cardiovascular diseases that are having high mortality rates worldwide. The early diagnosis of VHD prevents the development of cardiac diseases and allows for optimum medication. Despite of the ability of current gold standards in identifying VHD, they still lack the required accuracy and thus, several cases go misdiagnosed. In this vein
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Automatic medical protocol classification using machine learning approaches Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-16 Pilar López-Úbeda; Manuel Carlos Díaz-Galiano; Teodoro Martín-Noguerol; Antonio Luna; L. Alfonso Ureña-López; M. Teresa Martín-Valdivia
Background and objective: Assignment of medical imaging procedure protocols requires extensive knowledge about patient’s data, usually included in radiological request forms and radiological reports. Assignment of protocol is required prior to radiological study acquisition, determining procedure for each patient. The automation of this protocol assignment process could improve the efficiency of patient’s
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Fully automatic detection and classification of phytoplankton specimens in digital microscopy images Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-15 David Rivas-Villar; José Rouco; Rafael Carballeira; Manuel G. Penedo; Jorge Novo
Background and objectiveThe proliferation of toxin-producing phytoplankton species can compromise the quality of the water sources. This contamination is difficult to detect, and consequently to be neutralised, since normal water purification techniques are ineffective. Currently, the water analyses about phytoplankton are commonly performed by the specialists with manual routine analyses, which represents
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Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-15 Zeyu Wu; Zhaojun Xian; Wanru Ma; Qingsong Liu; Xusheng Huang; Baoyi Xiong; Shudong He; Wencheng Zhang
Background and Objective : The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors. Methods : Using a database based on 300 compounds, the 52 structure descriptors obtained based on the universal
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Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-15 Seyedehnafiseh Mirniaharikandehei; Morteza Heidari; Gopichandh Danala; Sivaramakrishnan Lakshmivarahan; Bin Zheng
Background and Objective Non-invasively predicting the risk of cancer metastasis before surgery can play an essential role in determining which patients can benefit from neoadjuvant chemotherapy. This study aims to investigate and test the advantages of applying a random projection algorithm to develop and optimize a radiomics-based machine learning model to predict peritoneal metastasis in gastric
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Long-Term Use of the Hybrid Artificial Pancreas by Adjusting Carbohydrate Ratios and Programmed Basal Rate: A Reinforcement Learning Approach Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-14 Adnan Jafar; Anas El Fathi; Ahmad Haidar
Background and objectives : The hybrid artificial pancreas regulates glucose levels in people with type 1 diabetes. It delivers (i) insulin boluses at meal times based on the meals’ carbohydrate content and the carbohydrate ratios (CRs) and (ii) insulin basal, between meals and at night, continuously modulated around individual-specific programmed basal rate. The CRs and programmed basal rate significantly
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Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-08 Defu Qiu; Yuhu Cheng; Xuesong Wang; Xiaoqiang Zhang
Background and objective With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the
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Automated Detection of Conduct Disorder and Attention Deficit Hyperactivity Disorder using Decomposition and Nonlinear Techniques with EEG Signals Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-14 Hui Tian Tor; Chui Ping Ooi; Nikki SJ Lim-Ashworth; Joel Koh En Wei; V Jahmunah; Shu Lih Oh; U Rajendra Acharya; Daniel Shuen Sheng Fung
Background and objectives: Attention deficit hyperactivity disorder (ADHD) is often presented with conduct disorder (CD). There is currently no objective laboratory test or diagnostic method to discern between ADHD and CD, and diagnosis is further made difficult as ADHD is a common neuro-developmental disorder often presenting with other co-morbid difficulties; and in particular with conduct disorder
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Kashin-Beck disease diagnosis based on deep learning from hand X-ray images Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-30 Jinyuan Dang; Hu Li; Kai Niu; Zhiyuan Xu; Jianhao Lin; Zhiqiang He
Background and objective Kashin-Beck Disease (KBD) is a serious endemic bone disease leading to short stature. The early radiological examinations are crucial for potential patients. However, many children in rural China cannot be diagnosed in time due to the shortage of professional orthopedists. In this paper, an algorithm is developed to automatically screening KBD based on hand X-ray images of
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Peristaltic activity for electro-kinetic complex driven cilia transportation through a non-uniform channel Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-04 Khurram Javid; Muhammad Riaz; Yu-Ming Chu; M. Ijaz Khan; Sami Ullah Khan; S. Kadry
Motivations: Now-a-days in medical science, the transport study of biological fluids through non-uniform vessels are going to increase due to their close relation to the reality. Motivated through such type of complex transportation, the current study is presented of cilia hydro-dynamics of an aqueous electrolytic viscous fluid through a non-uniform channel under an applied axial electric field. Mathematical
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Risk assessment of resurfacing implant loosening and femur fracture under low-energy impacts taking into account degenerative changes in bone tissues. Computer simulation Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-06 Galina M. Eremina; Alexey Yu Smolin
Background and objective Degenerative diseases of the musculoskeletal system significantly reduce the quality of human life. Hip resurfacing is used to treat degenerative diseases in the later stages. After surgery, there is a risk of endoprosthesis loosening and low-energy fracture during daily physical activity. Computer modeling is a promising way to predict the optimal low-energy loads that do
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Topological recovery for non-rigid 2D/3D registration of coronary artery models Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-05 Siyeop Yoon; Chang Hwan Yoon; Deukhee Lee
Background and Objective: Intra-operative X-ray angiography, the current standard method for visualizing and diagnosing cardiovascular disease, is limited in its ability to provide essential 3D information. These limitations are disadvantages in treating patients. For example, it is a cause of lowering the success rate of interventional procedures. Here, we propose a novel 2D-3D non-rigid registration
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Audio-based snore detection using deep neural networks Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-25 Jiali Xie; Xavier Aubert; Xi Long; Johannes van Dijk; Bruno Arsenali; Pedro Fonseca; Sebastiaan Overeem
Background and Objective: Snoring is a prevalent phenomenon. It may be benign, but can also be a symptom of obstructive sleep apnea (OSA) a prevalent sleep disorder. Accurate detection of snoring may help with screening and diagnosis of OSA. Methods: We introduce a snore detection algorithm based on the combination of a convolutional neural network (CNN) and a recurrent neural network (RNN). We obtained
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HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-10 Zhimin Yuan; Xiaofen Ma; Jiajin Yi; Zhengrong Luo; Jialin Peng
Background and objective: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural
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Particle Swarm Optimizer for Arterial Blood Flow Models Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-09 Yasser Aboelkassem; Dragana Savic
Background and objective: Mathematical modeling and computational simulations of arterial blood flow network can offer an insilico platform for both diagnostics and therapeutic phases of patients that suffer from cardiac diseases. These models are normally complex and involve many unknown parameters. For physiological relevance, these parameters should be optimized using in-vivo human/animal data sets
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Adaptive finite element eye model for the compensation of biometric influences on acoustic tonometry Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-09 Jan Osmers; Nils Kaiser; Michael Sorg; Andreas Fischer
Background and Objective: Glaucoma is currently a major cause for irreversible blindness worldwide. A risk factor and the only therapeutic control parameter is the intraocular pressure (IOP). The IOP is determined with tonometers, whose measurements are inevitably influenced by the geometry of the eye. Even though the corneal mechanics have been investigated to improve accuracy of Goldmann and air
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Adaptive Learning and Cross Training Improves R-Wave Detection in ECG Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-08 Nagarajan Ganapathy; Ramakrishnan Swaminathan; Thomas. M. Deserno
Background and Objective Automated R-wave detection plays a vital role in electrocardiography (ECG) and ECG-based computer-aided diagnosis. Recently, a multi-level one-dimensional (1D) deep learning approach was presented that shows good performance as compared to traditional methods. Methods In this paper, we present several improvements of the multi-level 1D convolutional neural network (CNN)-based
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Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2021-01-07 Lazaros Tsochatzidis; Panagiota Koutla; Lena Costaridou; Ioannis Pratikakis
Background and ObjectivesSegmentation of mammographic lesions has been proven to be a valuable source of information, as it can assist in both extracting shape-related features and providing accurate localization of the lesion. In this work, a methodology is proposed for integrating mammographic mass segmentation information into a convolutional neural network (CNN), aiming to improve the diagnosis
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Determination of the representative static loads for cyclically repeated dynamic loads: a case study of bone remodeling simulation with gait loads Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-31 Bong Ju Chun; In Gwun Jang
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Effect of the birthing position on its evolution from a biomechanical point of view Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-29 Margarida Borges; Rita Moura; Dulce Oliveira; Marco Parente; Teresa Mascarenhas; Renato Natal
Background and objective During vaginal delivery, several positions can be adopted by the mother to be more comfortable and to help the labor process. The positions chosen are very influenced by factors such as monitoring and intervention during the second stage of labor. However, there is limited evidence to support the most ideal birthing position. This work aims at contributing to a better knowledge
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Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-11-21 Chiara Tacchino; Martina Impagliazzo; Erika Maggi; Marta Bertamino; Isa Blanchi; Francesca Campone; Paola Durand; Marco Fato; Psiche Giannoni; Riccardo Iandolo; Massimiliano Izzo; Pietro Morasso; Paolo Moretti; Luca Ramenghi; Keisuke Shima; Koji Shimatani; Toshio Tsuji; Sara Uccella; Maura Casadio
Background and Objectives: The number of preterm babies is steadily growing world-wide and these neonates are at risk of neuro-motor-cognitive deficits. The observation of spontaneous movements in the first three months of age is known to predict such risk. However, the analysis by specifically trained physiotherapists is not suited for the clinical routine, motivating the development of simple computerized
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Pathological myopia classification with simultaneous lesion segmentation using deep learning Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-28 Ruben Hemelings; Bart Elen; Matthew B. Blaschko; Julie Jacob; Ingeborg Stalmans; Patrick De Boever
Background and Objectives Pathological myopia (PM) is the seventh leading cause of blindness, with a reported global prevalence up to 3%. Early and automated PM detection from fundus images could aid to prevent blindness in a world population that is characterized by a rising myopia prevalence. We aim to assess the use of convolutional neural networks (CNNs) for the detection of PM and semantic segmentation
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Effects of porosity in four-layered non-linear blood rheology in constricted narrow arteries with clinical applications Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-22 Afiqah Wajihah S.; D.S. Sankar
Background and objective: This study aims to investigate the haemodynamical factors with the motive to spell out some useful information for better interpretation and treatment of cardiovascular diseases. Numerous researchers theoretically investigated the movement of blood in the vascular system, treating blood as either single-layered or two-layered fluid representation. In this contemporary study
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Mathematical modelling, parameter estimation and computational simulation for skin wound healing under Copaiferalangsdorffi treatments Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-26 Marta H. de Oliveira; Lucas F.S. Gushiken; Cláudia H. Pellizzon; Paulo F.A. Mancera
We present three mathematical models which simulate the wound healing time for 10% oil-resin (10% OR), 10% hydroalcoholic extract (10% EH) (Copaifera langsdorffii drugs), Lanette cream (LC) and Collagenase treatments. Wound healing is a complex process consisting of inflammatory, proliferative and remodelling phases. The experiments were made on rats with wounds on their backs. The mathematical models
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Solitary Pulmonary nodule segmentation based on pyramid and improved grab cut Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-18 Dan Wang; Kun He; Bin Wang; Xiaoju Liu; Jiliu Zhou
Background and objective Accurate segmentation of solitary pulmonary nodule of digital radiography image is essential for lesion appearance measurement and medical follow-up. However, the imaging characteristics of digital radiography, the inhomogeneity and fuzzy contours of nodules often lead to poor performances. This work aims to develop a segmentation framework that satisfies the requirements of
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Optimal Deep Belief Network with Opposition based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-11 Mrs. M. Mary Adline Priya; Dr. S. Joseph Jawhar; Geisa J. Merry
Background and Objective This research proposes a successful method of extracting Gray-Level Co-occurrence Matrix (GLCM) picture handling models to classify low-and high-metastatic cancer organisms with five prevalent cancer cell line pairs, coupled with the scanning laser picture projection technique and the typical textural function, i.e. contrast, correlation, power, temperature and homogeneity
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Comparative analysis of active contour and convolutional neural network in rapid left-ventricle volume quantification using echocardiographic imaging Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-17 Xiliang Zhu; Yang Wei; Yu Lu; Ming Zhao; Ke Yang; Shiqian Wu; Hui Zhang; Kelvin K.L. Wong
In cardiology, ultrasound is often used to diagnose heart disease associated with myocardial infarction. This study aims to develop robust segmentation techniques for segmenting the left ventricle (LV) in ultrasound images to check myocardium movement during heartbeat. The proposed technique utilizes machine learning (ML) techniques such as the active contour (AC) and convolutional neural networks
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Brain MRI artefact detection and correction using convolutional neural networks Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-23 Ilkay Oksuz
Background and Objective: Brain MRI is one of the most commonly used diagnostic imaging tools to detect neurodegenerative disease. Diagnostic image quality is a key factor to enable robust image analysis algorithms developed for downstream tasks such as segmentation. In clinical practice, one of the main challenges is the presence of image artefacts, which can lead to low diagnostic image quality.
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Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal model-guided tracking method Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-22 Hongwei Du; Kai Shao; Fangxun Bao; Yunfeng Zhang; Chengyong Gao; Wei Wu; Caiming Zhang
Background and objective Accurate coronary artery tree segmentation can now be developed to assist radiologists in detecting coronary artery disease. In clinical medicine, the noise, low contrast, and uneven intensity of medical images along with complex shapes and vessel bifurcation structures make coronary artery segmentation challenging. In this work, we propose a multiobjective clustering and toroidal
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Iterative closest graph matching for non-rigid 3D/2D coronary arteries registration Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-22 Jianjun Zhu; Heng Li; Danni Ai; Qi Yang; Jingfan Fan; Yong Huang; Hong Song; Yechen Han; Jian Yang
Background and objective Fusion of the preoperative computed tomography angiography and intraoperative X-ray angiography images can considerably enhance the visual perception of physicians during percutaneous coronary interventions. This technique can provide 3D information of the arteries and reduce the uncertainty of 2D guidance images. For this purpose, 3D/2D vascular registration with high accuracy
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Methodology for Estimation OF annual risk of rupture for abdominal aortic aneurysm Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-25 Stanislav Polzer; Jan Kracík; Tomáš Novotný; Luboš Kubíček; Robert Staffa; Madhavan L. Raghavan
Background and Objective: Estimating patient specific annual risk of rupture of abdominal aortic aneurysm (AAA) is currently based only on population. More accurate knowledge based on patient specific data would allow surgical treatment of only those AAAs with significant risk of rupture. This would be beneficial for both patients and health care system. Methods: A methodology for estimating annual
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Virtual patients for mechanical ventilation in the intensive care unit Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-18 Cong Zhou; J. Geoffrey Chase; Jennifer Knopp; Qianhui Sun; Merryn Tawhai; Knut Möller; Serge J Heines; Dennis C. Bergmans; Geoffrey M. Shaw; Thomas Desaive
Background Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised
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Ultrasonic thyroid nodule detection method based on U-Net network Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-17 Chen Chu; Jihui Zheng; Yong Zhou
Objective Aiming at the time consuming processing of existing thyroid nodule detection and difficulty in feature extraction, U-Net-based thyroid nodule detection is proposed to perform computed aided diagnosis. Method This paper proposes a mark-guided ultrasound deep network segmentation model of thyroid nodules. By comparing with VGG19, Inception V3, DenseNet 161, segmentation accuracy, segmentation
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Seizure onset detection based on detection of changes in brain activity quantified by evolutionary game theory model Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-17 Ramtin Hamavar; Babak Mohammadzadeh Asl
Background and Objective: Epilepsy is one of the most common diseases of the nervous system, affecting about 1% of the world’s population. The unpredictable nature of the epilepsy seizures deprives the patients and those around them of living a normal life. Therefore, the development of new methods that can help these patients will increase the life quality of these people and can bring a lot of economic
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Retinal image registration as a tool for supporting clinical applications Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-17 Carlos Hernandez-Matas; Xenophon Zabulis; Antonis A. Argyros
Background and Objective: The study of small vessels allows for the analysis and diagnosis of diseases with strong vasculopathy. This type of vessels can be observed non-invasively in the retina via fundoscopy. The analysis of these vessels can be facilitated by applications built upon Retinal Image Registration (RIR), such as mosaicing, Super Resolution (SR) or eye shape estimation. RIR is challenging
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Spatial mechanistic modeling for prediction of the growth of asymptomatic meningiomas Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-11-07 Annabelle Collin; Cédrick Copol; Vivien Pianet; Thierry Colin; Julien Engelhardt; Guy Kantor; Hugues Loiseau; Olivier Saut; Benjamin Taton
Background and Objective Mathematical modeling of tumor growth draws interest from the medical community as they have the potential to improve patients’ care and the use of public health resources. The main objectives of this work are to model the growth of meningiomas – slow-growing benign tumors requiring extended imaging follow-up – and to predict tumor volume and shape at a later desired time using
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Effect of transport parameters on atherosclerotic lesion growth: A parameter sensitivity analysis Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-17 Ratchanon Piemjaiswang; Yan Ding; Yuqing Feng; Pornpote Piumsomboon; Benjapon Chalermsinsuwan
Background Atherosclerosis is a degenerative disease of the arterial wall. It results in the formation of progressively growing plaque lesions that can harden and narrow their host arteries. Current computational models of the inflammatory process that govern atherosclerosis growth are reliant on a number of parameters that can freely vary and whose precise values are not well known. Methods To identify
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Numerical analysis of non-Fourier thermal response of lung tissue based on experimental data with application in laser therapy Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-18 Iman Eltejaei; Mohsen Balavand; Afsaneh Mojra
Background and objective The thermal therapy is a minimally invasive technique used as an alternative approach to conventional cancer treatments. There is an increasing concern about the accuracy of the thermal simulation during the process of tumor ablation. This study is aimed at investigating the effect of finite speed of heat propagation in the biological lung tissue, experimentally and numerically
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Discrimination of simultaneous psychological and physical stressors using wristband biosignals Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-17 Mert Sevil; Mudassir Rashid; Iman Hajizadeh; Mohammad Reza Askari; Nicole Hobbs; Rachel Brandt; Minsun Park; Laurie Quinn; Ali Cinar
Background and objective: In this work, we address the problem of detecting and discriminating acute psychological stress (APS) in the presence of concurrent physical activity (PA) using wristband biosignals. We focused on signals available from wearable devices that can be worn in daily life because the ultimate objective of this work is to provide APS and PA information in real-time management of
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The Role of the Internet of Things in Healthcare: Future Trends and Challenges Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-13 Zahra Nasiri Aghdam; Amir Masoud Rahmani; Mehdi Hosseinzadeh
Background and Objective With the recent advances in the Internet of Things (IoT), the field has become more and more developed in healthcare. The Internet of things will help physicians and hospital staff perform their duties comfortably and intelligently. With the latest advanced technologies, most of the challenges of using IoT have been resolved, and this technology can be a great revolution and
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Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-22 Manuel M. Eichenlaub; John G. Hattersley; Mary C. Gannon; Frank Q. Nuttall; Natasha A. Khovanova
Background and objective The oral minimal model of glucose dynamics is one of the most prominent methods for assessing postprandial glucose metabolism. The model yields estimates of insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data after an oral glucose challenge. Despite its success, the oral minimal modelling approach has several weaknesses that this paper
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Unsupervised sorting of retinal vessels using locally consistent Gaussian mixtures Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-05 D. Relan; R. Relan
Background and Objectives: Retinal blood vessels classification into arterioles and venules is a major task for biomarker identification. Especially, clustering of retinal blood vessels is a challenging task due to factors affecting the images such as contrast variability, non-uniform illumination etc. Hence, a high performance automatic retinal vessel classification system is highly prized. In this
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CMC: A consensus multi-view clustering model for predicting Alzheimer’s disease progression Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-08 Xiaobo Zhang; Yan Yang; Tianrui Li; Yiling Zhang; Hao Wang; Hamido Fujita
Machine learning has been used in the past for the auxiliary diagnosis of Alzheimer’s Disease (AD). However, most existing technologies only explore single-view data, require manual parameter setting and focus on two-class (i.e., dementia or not) classification problems. Unlike single-view data, multi-view data provide more powerful feature representation capability. Learning with multi-view data is
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Quantitative evaluation of liver fibrosis based on ultrasound radio frequency signals: An animal experimental study Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-02 Guangwen Cheng; Meng Dai; Tianlei Xiao; Tiantian Fu; Hong Han; Yuanyuan Wang; Wenping Wang; Hong Ding; Jinhua Yu
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Adversarial multi-source transfer learning in healthcare: Application to glucose prediction for diabetic people Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-11-30 Maxime De Bois; Mounîm A. El Yacoubi; Mehdi Ammi
Background and objectives: Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity through the use of transfer learning
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Adverse Drug Reaction extraction: Tolerance to entity recognition errors and sub-domain variants Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-05 Sara Santiso; Alicia Pérez; Arantza Casillas
• Background and Objective:This work tackles the Adverse Drug Reaction (ADR) extraction in Electronic Health Records (EHRs) written in Spanish. This task is within the framework of natural language processing. It consists of extracting relations between drug-disease pairs, with the drug as the causing agent of the reaction. To this end, a pipeline is employed: first, relevant clinical entities are
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An Intelligent Model for the Detection of White Blood Cells using Artificial Intelligence Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-05 Anita; Anupam Yadav
Background and Objective: The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images
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Human activity pattern implications for modeling SARS-CoV-2 transmission Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-08 Yulan Wang; Bernard Li; Ramkiran Gouripeddi; Julio C. Facelli
Background and Objectives SARS-CoV-2 emerged in December 2019 and rapidly spread into a global pandemic. Designing optimal community responses (social distancing, vaccination) is dependent on the stage of the disease progression, discovery of asymptomatic individuals, changes in virulence of the pathogen, and current levels of herd immunity. Community strategies may have severe and undesirable social
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Coronary angiography image segmentation based on PSPNet Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-04 Xiliang Zhu; Zhaoyun Cheng; Sheng Wang; Xianjie Chen; Guoqing Lu
Purpose: Coronary artery disease (CAD) is known to have high prevalence, high disability and mortality. The incidence and mortality of cardiovascular disease are also gradually increasing worldwide. Therefore, our paper proposes to use a more efficient image processing method to extract accurate vascular structures from vascular images by combining computer vision and deep learning. Method: Our proposed
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Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-10-23 Peiran Chen; Ruud J.G. van Sloun; Simona Turco; Hessel Wijkstra; Domenico Filomena; Luciano Agati; Patrick Houthuizen; Massimo Mischi
Background and Objective: Left ventricle (LV) dysfunction always occurs at early heart-failure stages, producing variations in the LV flow patterns. Cardiac diagnostics may therefore benefit from flow-pattern analysis. Several visualization tools have been proposed that require ultrafast ultrasound acquisitions. However, ultrafast ultrasound is not standard in clinical scanners. Meanwhile techniques
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Simulator-generated training datasets as an alternative to using patient data for machine learning: An example in myocardial segmentation with MRI Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-10-27 Christos G. Xanthis; Dimitrios Filos; Kostas Haris; Anthony H. Aletras
Background and Objective: Supervised Machine Learning techniques have shown significant potential in medical image analysis. However, the training data that need to be collected for these techniques in the field of MRI 1) may not be available, 2) may be available but the size is small, 3) may be available but not representative and 4) may be available but with weak labels. The aim of this study was
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Automatic stenosis recognition from coronary angiography using convolutional neural networks Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-11-02 Jong Hak Moon; Da Young Lee; Won Chul Cha; Myung Jin Chung; Kyu-Sung Lee; Baek Hwan Cho; Jin Ho Choi
Background and objective Coronary artery disease, which is mostly caused by atherosclerotic narrowing of the coronary artery lumen, is a leading cause of death. Coronary angiography is the standard method to estimate the severity of coronary artery stenosis, but is frequently limited by intra- and inter-observer variations. We propose a deep-learning algorithm that automatically recognizes stenosis
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In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times. Comput. Methods Programs Biomed. (IF 3.632) Pub Date : 2020-12-01 Michail-Antisthenis Tsompanas; Larry Bull; Andrew Adamatzky; Igor Balaz
Background and Objective: Cancer tumors constitute a complicated environment for conventional anti-cancer treatments to confront, so solutions with higher complexity and, thus, robustness to diverse conditions are required. Alternations in the tumor composition have been documented, as a result of a conventional treatment, making an ensemble of cells drug resistant. Consequently, a possible answer