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1
Arterial blood pressure feature estimation using photoplethysmography.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Armin Soltan Zadi , Raichel Alex , Rong Zhang , Donald E Watenpaugh , Khosrow Behbehani
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the estimates, as measured by the root mean square of the model residuals, was less than 5 mmHg during normal breathing and less than 8 mmHg during the breath-hold maneuver. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.
DOI : 10.1016/j.compbiomed.2018.09.013
Anahtar Kelimeler : Autoregulation, Blood pressure, Estimation, Hemodynamics, Modeling, SaO2, Sleep disorders
2
Automated localization and segmentation techniques for B-mode ultrasound images: A review.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Kristen M Meiburger , U Rajendra Acharya , Filippo Molinari
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
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Kaydet Alıntıla
B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.
DOI : 10.1016/j.compbiomed.2017.11.018
Anahtar Kelimeler : Automated, B-mode imaging, Localization, Review, Segmentation, Ultrasound
Sınıflandırma :
3
Genetic algorithm for assigning weights to gene expressions using functional annotations.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Shubhra Sankar Ray , Sampa Misra
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
A method, named genetic algorithm for assigning weights to gene expressions using functional annotations (GAAWGEFA), is developed to assign proper weights to the gene expressions at each time point. The weights are estimated using functional annotations of the genes in a genetic algorithm framework. The method shows gene similarity in an improved manner as compared with other existing methods because it takes advantage of the existing functional annotations of the genes. The weight combination for the expressions at different time points is determined by maximizing the fitness function of GAAWGEFA in terms of the positive predictive value (PPV) for the top 10,000 gene pairs. The performance of the proposed method is primarily compared with Biweight mid correlation (BICOR) and original expression values for the six Saccharomyces cerevisiae datasets and one Bacillus subtilis dataset. The utility of GAAWGEFA is shown in predicting the functions of 48 unclassified genes (using p-value cutoff 10-13) from Saccharomyces cerevisiae microarray data where the expressions are weighted using GAAWGEFA and are clustered using k-medoids algorithm. The related code along with various parameters is available at http://sampa.droppages.com/GAAWGEFA.html.
DOI : 10.1016/j.compbiomed.2018.11.011
Anahtar Kelimeler : Computational biology, Gene annotation, Gene expression, Genetic algorithm, Saccharomyces cerevisiae, Weighting
Sınıflandırma :
4
The zinc transporter SLC39A7 (ZIP7) harbours a highly-conserved histidine-rich N-terminal region that potentially contributes to zinc homeostasis in the endoplasmic reticulum.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
John Adulcikas , Shaghayegh Norouzi , Lawrence Bretag , Sukhwinder Singh Sohal , Stephen Myers
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
The zinc transporter SLC39A7 (ZIP7)1 is a resident endoplasmic reticulum (ER) protein that is involved in controlling the release of zinc from this organelle into the cytosol. Subsequently, zinc plays a major role in processes that preserve cellular homeostasis. The ER contains a high concentration of zinc, and under normal physiological responses, maintains ER function. Disturbances in the concentration and distribution of zinc in the ER leads to abnormal processes that typify many disease states. ZIP7 is protective against ER stress and is a critical 'gate-keeper' of zinc release from the ER during processes that require cellular maintenance. However, it is not known how ZIP7 achieves this protective activity while maintaining cellular function. Bioinformatics analysis was utilised to determine the relationship between ZIP7 and other zinc transporters across humans and the animal and plant kingdom to determine the structure of this transporter in binding zinc in the ER. Analysis of the amino acid sequence of ZIP7 revealed several potential histidine binding sites for zinc in the N-terminal region that were significantly different in comparison to the other members of this family. Moreover, this histidine-rich region in the N-terminal of ZIP7 was highly conserved across the animal and plant kingdom. Accordingly, the highly conserved histidine-rich region in the N-termini of ZIP7 across the animal and plant kingdom suggests that this domain has critical function(s). We hypothesise that ER-localized ZIP7 can potentially sequester zinc to these histidine-rich regions and therefore provides a mechanism that is protective of this cellular structure.
DOI : 10.1016/j.compbiomed.2018.07.007
Anahtar Kelimeler : Bioinformatics, Endoplasmic reticulum, ZIP7, Zinc, Zinc transporters
Sınıflandırma :
5
A new integrated and interactive tool applicable to inborn errors of metabolism: Application to alkaptonuria.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Ottavia Spiga , Vittoria Cicaloni , Andrea Zatkova , Lia Millucci , Giulia Bernardini , Andrea Bernini , Barbara Marzocchi , Monica Bianchini , Andrea Zugarini , Alberto Rossi , Matteo Zazzeri , Alfonso Trezza , Bruno Frediani , Lakshminarayan Ranganath , Daniela Braconi , Annalisa Santucci
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
This paper describes our experience with the development and implementation of a database for the rare disease Alkaptonuria (AKU, OMIM: 203500). AKU is an autosomal recessive disorder caused by a gene mutation leading to the accumulation of homogentisic acid (HGA). Analogously to other rare conditions, currently there are no approved biomarkers to monitor AKU progression or severity. Although some biomarkers are under evaluation, an extensive biomarker analysis has not been undertaken in AKU yet. In order to fill this gap, we gained access to AKU-related data that we carefully processed, documented and stored in a database, which we named ApreciseKUre. We undertook a suitable statistical analysis by associating every couple of potential biomarkers to highlight significant correlations. Our database is continuously updated allowing us to find novel unpredicted correlations between AKU biomarkers and to confirm system reliability. ApreciseKUre includes data on potential biomarkers, patients' quality of life and clinical outcomes facilitating their integration and possibly allowing a Precision Medicine approach in AKU. This framework may represent an online tool that can be turned into a best practice model for other rare diseases.
DOI : 10.1016/j.compbiomed.2018.10.002
Anahtar Kelimeler : Alkaptonuria, Biomarkers, Data analysis, Database, Precision medicine, Rare disease
Sınıflandırma :
6
A vectorized Levenberg-Marquardt model fitting algorithm for efficient post-processing of cardiac T1 mapping MRI.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Shufang Liu , Aurelien Bustin , Pauline Ferry , Andrei Codreanu , Darius Burschka , Anne Menini , Freddy Odille
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
T1 mapping is an emerging MRI research tool to assess diseased myocardial tissue. Recent research has been focusing on the image acquisition protocol and motion correction, yet little attention has been paid to the curve fitting algorithm.
DOI : 10.1016/j.compbiomed.2018.03.009
Anahtar Kelimeler : Cardiac MR, Curve fitting, MR parameter mapping, Relaxometry, T(1) mapping
Sınıflandırma :
7
Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Elisa Cuadrado-Godia , Md Maniruzzaman , Tadashi Araki , Anudeep Puvvula , Md Jahanur Rahman , Luca Saba , Harman S Suri , Ajay Gupta , Sumit K Banchhor , Jagjit S Teji , Tomaž Omerzu , Narendra N Khanna , John R Laird , Andrew Nicolaides , Sophie Mavrogeni , George D Kitas , Jasjit S Suri
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
This study examines the association between six types of carotid artery disease image-based phenotypes and HbA1c in diabetes patients. Six phenotypes (intima-media thickness measurements (cIMT (ave.), cIMT (max.), cIMT (min.)), bidirectional wall variability (cIMTV), morphology-based total plaque area (mTPA), and composite risk score (CRS)) were measured in an automated setting using AtheroEdge™ (AtheroPoint, CA, USA).
DOI : 10.1016/j.compbiomed.2018.08.008
Anahtar Kelimeler : AtheroEdge™, Automated six carotid risk phenotypes, Carotid morphology, Diabetes, Inter-operator variability, Logistic regression, Odds ratio, Ultrasound
Sınıflandırma :
8
Hypersampling of pseudo-periodic signals by analytic phase projection.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Henning U Voss
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
A method to upsample insufficiently sampled experimental time series of pseudo-periodic signals is proposed. The result is an estimate of the pseudo-periodic cycle underlying the signal. This "hypersampling" requires a sufficiently sampled reference signal that defines the pseudo-periodic dynamics. The time series and reference signal are combined by projecting the time series values to the analytic phase of the reference signal. The resulting estimate of the pseudo-periodic cycle has a considerably higher effective sampling rate than the time series. The procedure is applied to time series of MRI images of the human brain. As a result, the effective sampling rate could be increased by three orders of magnitude. This allows for capturing the waveforms of the very fast cerebral pulse waves traversing the brain. Hypersampling is numerically compared to the more commonly used retrospective gating. An outlook regarding EEG and optical recordings of brain activity as the reference signal is provided.
DOI : 10.1016/j.compbiomed.2018.05.008
Anahtar Kelimeler : Applications of biomedical signal processing, EEG, Hilbert transform, MRI, Monocomponent signal, Optical imaging, Pulse wave analysis, Signal analysis
Sınıflandırma :
9
Left atrial appendage segmentation from 3D CCTA images for occluder placement procedure.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Hrvoje Leventić , Danilo Babin , Lazar Velicki , Daniel Devos , Irena Galić , Vladimir Zlokolica , Krešimir Romić , Aleksandra Pižurica
Yıl :
2018
Comput. Biol. Med.
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 Piri Tam Metin Bulucu
Kaydet Alıntıla
Percutaneous left atrial appendage (LAA) closure (placement of an occluder to close the appendage) is a novel procedure for stroke prevention in patients suffering from atrial fibrillation. The closure procedure planning requires accurate LAA measurements which can be obtained from computed tomography (CT) images.
DOI : 10.1016/j.compbiomed.2018.11.006
Anahtar Kelimeler : Image analysis, Image segmentation, Left atrial appendage, Left atrial appendage closure, Left atrial appendage occlusion
Sınıflandırma :
10
Focus on an infrequently used quantity in the context of competing risks: The conditional probability function.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Bastien Cabarrou , Florence Dalenc , Eve Leconte , Jean-Marie Boher , Thomas Filleron
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
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Kaydet Alıntıla
In clinical studies of hematologic and oncologic diseases, the outcomes of interest are generally composite time to event endpoints which are usually defined by occurrence of different event types. Nonetheless, clinicians are interested in studying only one event type, which leads to a competing risks situation. In this context, Pepe and Mori presented a quantity directly derived from the cumulative incidence: the conditional probability. This function defines the probability that a given event occurs, conditionally on not having had a competing event by that time. The objective of this paper is to present this conditional cumulative incidence function and to compare its use to the cumulative incidence in different data sets. Different scenarios highlight the importance of the competing event on the interpretation of the conditional probability. Conditional probability needs to be interpreted jointly with the cumulative incidence. This quantity can be of interest especially when the risk of the competing event is large, strongly precludes the risk of the event of interest and provides useful additional information.
DOI : 10.1016/j.compbiomed.2018.08.002
Anahtar Kelimeler : Competing risks, Conditional probability, Cumulative incidence, Metastatic breast cancer, Regression model
Sınıflandırma :
11
The utility of routine clinical 12-lead ECG in assessing eligibility for subcutaneous implantable cardioverter defibrillator.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Jason A Thomas , Erick Andres Perez-Alday , Christopher Hamilton , Muammar M Kabir , Eugene A Park , Larisa G Tereshchenko
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
The subcutaneous implantable cardioverter-defibrillator (S-ICD) is a life-saving device. Recording of a specialized 3-lead electrocardiogram (ECG) is required for S-ICD eligibility assessment. The goals of this study were: (1) evaluate the effect of ECG filtering on S-ICD eligibility, and (2) simplify S-ICD eligibility assessment by development of an S-ICD ineligibility prediction tool, which utilizes the widely available routine 12-lead ECG.
DOI : 10.1016/j.compbiomed.2018.05.002
Anahtar Kelimeler : Electrocardiogram, Eligibility, Subcutaneous ICD
Sınıflandırma :
12
Projective geometric model for automatic determination of X-ray-emitting source of a standard radiographic system.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Laura García-Ruesgas , Rafael Álvarez-Cuervo , Francisco Valderrama-Gual , José Ignacio Rojas-Sola
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
Currently, many orthopedic operations are planned by analyzing X-rays. The exact position of the focus is needed to calculate the real size of an object that is represented in conical projection, although in practice, this position is difficult to determine using current X-ray commercial systems. In this paper, a new geometric model is proposed in order to determine accurately, practically, and economically the location of the emitting source of commercial imaging systems using a single standard X-ray image.
DOI : 10.1016/j.compbiomed.2018.06.016
Anahtar Kelimeler : MATLAB, Projective geometric model, Specific locator reference object, Standard radiograph, X-ray focus emitter
Sınıflandırma :
13
SSVEP recognition by modeling brain activity using system identification based on Box-Jenkins model.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Seyed Mohammad Mehdi Safi , Mohammad Pooyan , Ali Motie Nasrabadi
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
The steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received increasing attention in recent years. The present study proposes a new method for recognition based on system identification. The method relies on modeling the electroencephalogram (EEG) signals using the Box-Jenkins model. In this approach, the recorded EEG signal is considered as a combination of an SSVEP signal evoked by periodic visual stimulation and a background EEG signal whose components are modeled by a moving average (MA) process and an auto-regressive moving average (ARMA) process, respectively. Then, the target frequency is determined by comparing the modeled SSVEP signals for all stimulation frequencies. The experimental results of the proposed method for recorded EEG signals from five subjects (each subject with four stimulation frequencies) demonstrated a significant improvement in the accuracy of the SSVEP recognition in contrast to canonical correlation analysis, least absolute shrinkage and selection operator, and multivariate linear regression methods. The proposed method exhibits enhanced accuracy especially for short data length and a small number of channels. This superiority suggests that the proposed method is an appropriate choice for the implementation of real-time SSVEP based BCI systems.
DOI : 10.1016/j.compbiomed.2018.08.011
Anahtar Kelimeler : Box-jenkins model (BJM), Brain-computer interface (BCI), Canonical correlation analysis (CCA), Least absolute shrinkage and selection operator (LASSO), Multivariate linear regression (MLR), Steady-state visual evoked potential (SSVEP)
Sınıflandırma :
14
Prognostication of metastatic death in uveal melanoma patients: A Markov multi-state model.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Antonio Eleuteri , Azzam F G Taktak , Sarah E Coupland , Heinrich Heimann , Helen Kalirai , Bertil Damato
Yıl :
2018
Comput. Biol. Med.
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Kaydet Alıntıla
Uveal melanoma is fatal in almost 50% of patients. We previously developed a prognostic model to predict all-cause mortality. The aim of this study was to improve our model by predicting metastatic death as a cause-specific event distinct from other causes of death.
DOI : 10.1016/j.compbiomed.2018.09.024
Anahtar Kelimeler : Bootstrap, Competing risks, Cox model, Markov model, Multiple imputation, Uveal melanoma
Sınıflandırma :
15
Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Edgar García-Cano , Fernando Arámbula Cosío , Luc Duong , Christian Bellefleur , Marjolaine Roy-Beaudry , Julie Joncas , Stefan Parent , Hubert Labelle
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
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Kaydet Alıntıla
The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provide the right treatment to prevent curve progression.
DOI : 10.1016/j.compbiomed.2018.09.029
Anahtar Kelimeler : Adolescent idiopathic scoliosis, Independent component analysis, Machine learning, Prediction of spinal curve progression, Random forest
Sınıflandırma :
16
Coeliac disease under a microscope: Histological diagnostic features and confounding factors.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Giulia Gibiino , Loris Lopetuso , Riccardo Ricci , Antonio Gasbarrini , Giovanni Cammarota
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Coeliac disease (CD) and gluten-related disorders represent an important cornerstone of the daily practice of gastroenterologists, endoscopists and dedicated histopathologists. Despite the knowledge of clinical, serological and histological typical lesions, there are some conditions to consider for differential diagnosis. From the first description of histology of CD, several studies were conducted to define similar findings suggestive for microscopic enteritis. Considering the establishment of early precursor lesions, the imbalance of gut microbiota is another point still requiring a detailed definition. This review assesses the importance of a right overview in case of suspected gluten-related disorders and the several conditions mimicking a similar histology.
DOI : 10.1016/j.compbiomed.2018.10.020
Anahtar Kelimeler : Gluten, Gut microbiota, Intraepithelial lymphocyte (IEL), Microscopic enteritis, Non-coeliac gluten-sensitivity
Sınıflandırma :
17
Computational simulation of TEVAR in the ascending aorta for optimal endograft selection: A patient-specific case study.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
R M Romarowski , M Conti , S Morganti , V Grassi , M M Marrocco-Trischitta , S Trimarchi , F Auricchio
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Thoracic endovascular aortic repair of the ascending aorta is becoming an option for patients considered unfit for open surgery. Such an endovascular procedure requires careful pre-operative planning and the customization of prosthesis design. The patient-specific tailoring of the procedure may call for dedicated tools to investigate virtual treatment scenarios. Given such considerations, the present study shows a computational framework for choosing and deploying stent-grafts via Finite Element Analysis, by supporting the device sizing and selection in a real case dealing with the endovascular treatment of a pseudoaneurysm. In particular, three devices with various lengths and materials were examined. Two off-the-shelf devices were computationally tested: one composed of Stainless Steel rings with a nominal length of 60 mm and another one with Nitinol rings and a distal free flow extension, with a nominal length of 70 mm. In third place, a custom-made stent-graft, also with Nitinol rings and containing both proximal and distal bare extensions with a nominal length of 75 mm, was deployed. The latter solution based on patient morphology and virtually benchmarked in this simulation framework, enhanced the apposition to the wall by reducing the distance between the skirt and the vessel from more than 6 mm to less than 2 mm in the distal sealing zone. Our experience shows that in-silico simulations can help choosing the right endograft for the ascending aorta as well as the right deployment sequence. This process may also encourage vendors to develop new devices for cases where open repair is unfeasible.
DOI : 10.1016/j.compbiomed.2018.10.014
Anahtar Kelimeler : Ascending aorta, Computational simulations, Finite element analysis, TEVAR
Sınıflandırma :
18
Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Joel E W Koh , Eddie Y K Ng , Sulatha V Bhandary , Yuki Hagiwara , Augustinus Laude , U Rajendra Acharya
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Untreated age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma may lead to irreversible vision loss. Hence, it is essential to have regular eye screening to detect these eye diseases at an early stage and to offer treatment where appropriate. One of the simplest, non-invasive and cost-effective techniques to screen the eyes is by using fundus photo imaging. But, the manual evaluation of fundus images is tedious and challenging. Further, the diagnosis made by ophthalmologists may be subjective. Therefore, an objective and novel algorithm using the pyramid histogram of visual words (PHOW) and Fisher vectors is proposed for the classification of fundus images into their respective eye conditions (normal, AMD, DR, and glaucoma). The proposed algorithm extracts features which are represented as words. These features are built and encoded into a Fisher vector for classification using random forest classifier. This proposed algorithm is validated with both blindfold and ten-fold cross-validation techniques. An accuracy of 90.06% is achieved with the blindfold method, and highest accuracy of 96.79% is obtained with ten-fold cross-validation. The highest classification performance of our system shows the potential of deploying it in polyclinics to assist healthcare professionals in their initial diagnosis of the eye. Our developed system can reduce the workload of ophthalmologists significantly.
DOI : 10.1016/j.compbiomed.2017.11.019
Anahtar Kelimeler : Age-related macular degeneration, Bag-of-visual-words, Computer-aided diagnosis system, Diabetic retinopathy, Eye diseases, Fisher vector encoder, Fundus images, Glaucoma, Machine learning
Sınıflandırma :
19
Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Sumit K Banchhor , Narendra D Londhe , Tadashi Araki , Luca Saba , Petia Radeva , Narendra N Khanna , Jasjit S Suri
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins.
DOI : 10.1016/j.compbiomed.2018.08.017
Anahtar Kelimeler : Atherosclerosis, Calcium, Carotid, Coronary, Heart disease, Intravascular, Morphology, Risk stratification, Stroke
Sınıflandırma :
20
A study of time-frequency features for CNN-based automatic heart sound classification for pathology detection.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Baris Bozkurt , Ioannis Germanakis , Yannis Stylianou
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
This study concerns the task of automatic structural heart abnormality risk detection from digital phonocardiogram (PCG) signals aiming at pediatric heart disease screening applications. Recently, various systems based on convolutional neural networks trained on time-frequency representations of segmental PCG frames have been presented that outperform systems using hand-crafted features. This study focuses on the segmentation and time-frequency representation components of the CNN-based designs. We consider the most commonly used features (MFCC and Mel-Spectrogram) used in state-of-the-art systems and a time-frequency representation influenced by domain-knowledge, namely sub-band envelopes as an alternative feature. Via tests carried on two high quality databases with a large set of possible settings, we show that sub-band envelopes are preferable to the most commonly used features and period synchronous windowing is preferable over asynchronous windowing.
DOI : 10.1016/j.compbiomed.2018.06.026
Anahtar Kelimeler : Automated cardiac auscultation, Heart disease screening, Heart sound classification, Phonocardiogram analysis, Time-frequency features
Sınıflandırma :
21
3-D segmentation of lung nodules using hybrid level sets.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Hina Shakir , Tariq Mairaj Rasool Khan , Haroon Rasheed
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Lung nodule segmentation in CT images and its subsequent volume analysis can help determine the malignancy status of a lung nodule. While several efficient segmentation schemes have been proposed, only a few studies evaluated the segmentation's performance for large nodules. In this research, we contribute a semi-automatic system which is capable of performing robust 3-D segmentations on both small and large nodules with good accuracy. The target CT volume is de-noised with an anisotropic diffusion filter and a region of interest is selected around the target nodule on a reference slice. The proposed model performs nodule segmentation by incorporating a mean intensity based threshold in Geodesic Active Contour model in level sets. We also devise an adaptive technique using image intensity histogram to estimate the desired mean intensity of the nodule. The proposed system is validated on both lung nodules and phantoms collected from publicly available diverse databases. Quantitative and visual comparative analysis of the proposed work with the Chan-Vese algorithm and statistic active contour model of 3D Slicer platform is also presented. The resulting mean spatial overlap between segmented nodules and reference nodules is 0.855, the mean volume bias is 0.10±0.2 ml and the algorithm repeatability is 0.060 ml. The achieved results suggest that the proposed method can be used for volume estimations of small as well as large-sized nodules.
DOI : 10.1016/j.compbiomed.2018.03.015
Anahtar Kelimeler : Active contours, Hybrid deformable model, Hybrid level-sets, Nodule segmentation
Sınıflandırma :
22
Towards understanding the complexity of cardiovascular oscillations: Insights from information theory.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Michal Javorka , Jana Krohova , Barbora Czippelova , Zuzana Turianikova , Zuzana Lazarova , Radovan Wiszt , Luca Faes
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Cardiovascular complexity is a feature of healthy physiological regulation, which stems from the simultaneous activity of several cardiovascular reflexes and other non-reflex physiological mechanisms. It is manifested in the rich dynamics characterizing the spontaneous heart rate and blood pressure variability (HRV and BPV). The present study faces the challenge of disclosing the origin of short-term HRV and BPV from the statistical perspective offered by information theory. To dissect the physiological mechanisms giving rise to cardiovascular complexity in different conditions, measures of predictive information, information storage, information transfer and information modification were applied to the beat-to-beat variability of heart period (HP), systolic arterial pressure (SAP) and respiratory volume signal recorded non-invasively in 61 healthy young subjects at supine rest and during head-up tilt (HUT) and mental arithmetics (MA). Information decomposition enabled to assess simultaneously several expected and newly inferred physiological phenomena, including: (i) the decreased complexity of HP during HUT and the increased complexity of SAP during MA; (ii) the suppressed cardiorespiratory information transfer, related to weakened respiratory sinus arrhythmia, under both challenges; (iii) the altered balance of the information transferred along the two arms of the cardiovascular loop during HUT, with larger baroreflex involvement and smaller feedforward mechanical effects; and (iv) an increased importance of direct respiratory effects on SAP during HUT, and on both HP and SAP during MA. We demonstrate that a decomposition of the information contained in cardiovascular oscillations can reveal subtle changes in system dynamics and improve our understanding of the complexity changes during physiological challenges.
DOI : 10.1016/j.compbiomed.2018.05.007
Anahtar Kelimeler : Blood pressure variability, Causality, Complexity, Heart rate variability, Redundancy
Sınıflandırma :
23
Segmentation of histological images and fibrosis identification with a convolutional neural network.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Xiaohang Fu , Tong Liu , Zhaohan Xiong , Bruce H Smaill , Martin K Stiles , Jichao Zhao
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises 11 successive convolutional - rectified linear unit - batch normalization layers. It outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300,000) trainable parameters than the state-of-the-art, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored to the problem of concern, and may potentially be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment.
DOI : 10.1016/j.compbiomed.2018.05.015
Anahtar Kelimeler : Convolutional neural network, Deep learning, Fibrosis, Histology, Image segmentation
Sınıflandırma :
24
Corrigendum to A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images Comput. Biol. Med. 90C (2017) 98-115.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Georgios Leontidis
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
DOI : 10.1016/j.compbiomed.2018.11.015
Sınıflandırma :
25
Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Juntao Li , Yanyan Wang , Xuekun Song , Huimin Xiao
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Multi-class classification has attracted much attention in cancer diagnosis and treatment and many machine learning methods have emerged for addressing this issue recently. However, class imbalance and gene selection problems occur in classifying lung cancer data. In this paper, an adaptive multinomial regression with a sparse overlapping group lasso penalty is proposed to perform classification and grouped gene selection for lung cancer gene expression data. An overlapped grouping strategy with biological interpretability is proposed, which highlights the importance of gene groups from the minority classes. By using the conditional mutual information, the gene significance within each group is evaluated and the data-driven weights are constructed. Based on the grouping strategy and constructed weights, a regularized adaptive multinomial regression is presented and the solving algorithm is developed, which can not only select the important gene groups for each class in performing multi-class classification, but also adaptively select important genes within each group. The experiment results show that the proposed method significantly outperforms the other 6 methods on classification accuracy, and the selected genes are disease-causing genes for lung cancer.
DOI : 10.1016/j.compbiomed.2018.06.014
Anahtar Kelimeler : Imbalanced data, Multi-class classification, Overlapping group lasso, Weighted gene co-expression networks
Sınıflandırma :
26
Micro finite element analysis of dental implants under different loading conditions.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Petr Marcián , Jan Wolff , Ladislava Horáčková , Jozef Kaiser , Tomáš Zikmund , Libor Borák
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Osseointegration is paramount for the longevity of dental implants and is significantly influenced by biomechanical stimuli. The aim of the present study was to assess the micro-strain and displacement induced by loaded dental implants at different stages of osseointegration using finite element analysis (FEA). Computational models of two mandible segments with different trabecular densities were constructed using microCT data. Three different implant loading directions and two osseointegration stages were considered in the stress-strain analysis of the bone-implant assembly. The bony segments were analyzed using two approaches. The first approach was based on Mechanostat strain intervals and the second approach was based on tensile/compression yield strains. The results of this study revealed that bone surrounding dental implants is critically strained in cases when only a partial osseointegration is present and when an implant is loaded by buccolingual forces. In such cases, implants also encounter high stresses. Displacements of partially-osseointegrated implant are significantly larger than those of fully-osseointegrated implants. It can be concluded that the partial osseointegration is a potential risk in terms of implant longevity.
DOI : 10.1016/j.compbiomed.2018.03.012
Anahtar Kelimeler : Dental implants, Micro finite element analysis, Osseointegration, Strain intensity, Stress intensity
Sınıflandırma :
27
Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Mahsa Ghaffari , Lea Sanchez , Guoren Xu , Ali Alaraj , Xiaohong Joe Zhou , Fady T Charbel , Andreas A Linninger
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Accurate subject-specific vascular network reconstruction is a critical task for the hemodynamic analysis of cerebroarterial circulation. Vascular skeletonization and computational mesh generation for large sections of cerebrovascular trees from magnetic resonance angiography (MRA) is an error-prone, operator-dependent, and very time-consuming task. Validation of reconstructed computational models is essential to ascertain their accuracy and precision, which directly relates to the confidence of CFD computations performed on these meshes. The aim of this study is to generate an imaging segmentation pipeline to validate and quantify the spatial accuracy of computational models of subject-specific cerebral arterial trees. We used a recently introduced parametric structured mesh (PSM) generation method to automatically reconstruct six subject-specific cerebral arterial trees containing 1364 vessels and 571 bifurcations. By automatically extracting sampling frames for all vascular segments and bifurcations, we quantify the spatial accuracy of PSM against the original MRA images. Our comprehensive study correlates lumen area, pixel-based statistical analysis, area overlap and centerline accuracy measurements. In addition, we propose a new metric, the pointwise offset surface distance metric (PSD), to quantify the spatial alignment between dimensions of reconstructed arteries and bifurcations with in-vivo data with the ability to quantify the over- and under-approximation of the reconstructed models. Accurate reconstruction of vascular trees can a practical process tool for morphological analysis of large patient data banks, such as medical record files in hospitals, or subject-specific hemodynamic simulations of the cerebral arterial circulation.
DOI : 10.1016/j.compbiomed.2018.07.004
Anahtar Kelimeler : Cerebral arterial tree, Hausdorff distance, Mesh validation, Morphological analysis, Parametric structured mesh, Pointwise surface distance, Shape similarity index
Sınıflandırma :
28
Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Aki Koivu , Teemu Korpimäki , Petri Kivelä , Tapio Pahikkala , Mikko Sairanen
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population.
DOI : 10.1016/j.compbiomed.2018.05.004
Anahtar Kelimeler : Down syndrome, Multi-layer neural network, Predictive modeling, Prenatal risk assessment, Trisomy 21
Sınıflandırma :
29
A new nonparametric statistical approach to detect lumen and Media-Adventitia borders in intravascular ultrasound frames.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Ali Kermani , Ahmad Ayatollahi
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
 Piri Tam Metin Bulucu
Kaydet Alıntıla
Intravascular ultrasound (IVUS) imaging is widely known as a powerful interventional imaging modality for diagnosing atherosclerosis, and for treatment planning. In this regard, the detection of lumen and media-adventitia (MA) borders is considered to be a vital process. However, the manual detection of these two borders by the physician is cumbersome due to the large number of frames in a sequence. In addition, no approved universal automatic method has been presented so far due to the great diversity in the appearance of the coronary artery in the images acquired by different IVUS systems. To this end, the present study aimed to provide a new border search theory on the radial profile, based upon the nonparametric statistical approach, and to develop a generic and fully automatic three-step process for extracting the lumen and MA borders in IVUS frames based on the proposed theory. Thereafter, the proposed theory and three-step process were evaluated on synthetic images, as well as on a test set of standard publicly available images, respectively. The results showed that our three-step process could segment the borders with ≥0.82 and with ≥0.75 Jaccard measure (JM) to manual borders in IVUS frames acquired by the 20 MHz and 40 MHz probes, respectively. Based on the results, the lumen and MA borders can be extracted automatically, and the border extraction process can be implemented in parallel for a polar image due to the capability of the present proposed method to estimate the borders for each angle independently.
DOI : 10.1016/j.compbiomed.2018.10.024
Anahtar Kelimeler : Border detection, Cubic spline, Intravascular image, Nonparametric statistics, Ultrasound
Sınıflandırma :
30
Survey on deep learning for radiotherapy.
Dergi Makalesi

Dergi Makalesi
Open Access
Kaynak :
PubMed
Dergi:
Comput. Biol. Med.
Yazar(lar) :
Philippe Meyer , Vincent Noblet , Christophe Mazzara , Alex Lallement
Yıl :
2018
Comput. Biol. Med.
Yayıncı Web Sitesi
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Kaydet Alıntıla
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.
DOI : 10.1016/j.compbiomed.2018.05.018
Anahtar Kelimeler : Convolutional networks, Deep-learning, Radiotherapy
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