Automatic Feature Learning Using Multichannel ROI Based on Deep Structured Algorithms for Computerized Lung Cancer Diagnosis
This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists ’ markings, and 134668 samples were generated by rotating every slice of nodule images. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 13, 2017 Category: Bioinformatics Authors: Wenqing Sun, Bin Zheng, Wei Qian Source Type: research
Patient characteristics associated with differences in radiation exposure from pediatric abdomen-pelvis CT scans: a quantile regression analysis
Computed tomography (CT) is a widely used diagnostic tool in pediatric medicine. However, due to concerns regarding radiation exposure, it is essential to identify patient characteristics associated with higher radiation burden from CT imaging, in order to more effectively target efforts towards dose reduction. Our objective was to identify the effects of various demographic and clinical patient characteristics on radiation exposure from single abdomen/pelvis CT scans in children. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 13, 2017 Category: Bioinformatics Authors: Jennifer N. Cooper, Daniel L. Lodwick, Brent Adler, Choonsik Lee, Peter C. Minneci, Katherine J. Deans Source Type: research
Spatial Enhancement of ECG using Diagnostic Similarity Score based Lead Selective Multi-scale Linear Model
In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 11, 2017 Category: Bioinformatics Authors: Jiss J. Nallikuzhy, S. Dandapat Source Type: research
Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method
Background. Celiac disease is one of the most common diseases in the world. Capsule endoscopy is an alternative way to visualize the entire small intestine without invasiveness to the patient. It is useful to characterize celiac disease, but hours are need to manually analyze the retrospective data of a single patient. Computer-aided quantitative analysis by a deep learning method helps in alleviating the workload during analysis of the retrospective videos.Method. Capsule endoscopy clips from 6 celiac disease patients and 5 controls were preprocessed for training. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 8, 2017 Category: Bioinformatics Authors: Teng Zhou, Guoqiang Han, Bing Nan Li, Zhizhe Lin, Edward J Ciaccio, Peter H Green, Jing Qin Source Type: research
Electroporation of Tissue and Cells: a Three-Equation Model of Drug Delivery
In this study we introduce a novel three-equation model of transport that is able to distinguish the drug uptake in reversibly electroporated cells from that in irreversibly electroporated cells. In order to relate the permeability increases and the cell survival to the local electric field, sigmoidal functions are fit to published experimental data. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 6, 2017 Category: Bioinformatics Authors: Finbar Argus, Bradley Boyd, S.M. Becker Source Type: research
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Source: Computers in Biology and Medicine - April 1, 2017 Category: Bioinformatics Source Type: research
IJ-OpenCV: Combining ImageJ and OpenCV for Processing Images in Biomedicine
Background and Objective. The effective processing of biomedical images usually requires the interoperability of diverse software tools that have different aims but are complementary. The goal of this work is to develop a bridge to connect two of those tools: ImageJ, a program for image analysis in life sciences, and OpenCV, a computer vision and machine learning library.Methods. Based on a thorough analysis of ImageJ and OpenCV, we detected the features of these systems that could be enhanced, and developed a library to combine both tools, taking advantage of the strengths of each system. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 1, 2017 Category: Bioinformatics Authors: C ésar Dom, Jónathan Heras, Vico Pascual Source Type: research
Automatic classification of human sperm head morphology
Background and Objective: Infertility is a problem that affects up to 15% of couples worldwide with emotional and physiological implications and semen analysis is the first step in the evaluation of an infertile couple. Indeed the morphology of human sperm cells is considered to be a clinical tool dedicated to the fertility prognosis and serves, mainly, for making decisions regarding the options of assisted reproduction technologies. Therefore, a complete analysis of not only normal sperm but also abnormal sperm turns out to be critical in this context. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 1, 2017 Category: Bioinformatics Authors: Violeta Chang, Laurent Heutte, Caroline Petitjean, Steffen H ärtel, Nancy Hitschfeld Source Type: research
Influence of the fixation region of a press –fit hip endoprosthesis on the stress–strain state of the “bone–implant” system
Although significant progress has been made in the development of total hip replacement, behaviour of the femoral component of an endoprosthesis in relation to the type of its fixation in the bone is still not fully understood. In this paper, behaviour of the femoral bone and the stem prosthesis is studied taking into account different types of prosthesis fixation in the medullary canal of the femur under the action of functional loads. For an analysis, a three-dimensional model of a femur has been developed based on the results of a computed tomography. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - April 1, 2017 Category: Bioinformatics Authors: Ievgen Levadnyi, Jan Awrejcewicz, M árcio Fagundes Goethel, Alexander Loskutov Source Type: research
A Genetic Algorithm-Based Model for Longitudinal Changes Detection in White Matter Fiber-Bundles of Patient with Multiple Sclerosis
Analysis of white matter (WM) tissue is essential to understand the mechanisms of neurodegenerative pathologies like multiple sclerosis (MS). Recently longitudinal studies started to show how the temporal component is important to investigate temporal diffuse effects of neurodegenerative pathologies.Diffusion tensor imaging (DTI) constitutes one of the most sensitive technique for the detection and characterization of brain related pathological processes and allows also the reconstruction of WM fibers. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 30, 2017 Category: Bioinformatics Authors: Claudio Stamile, Gabriel Kocevar, Fran çois Cotton, Dominique Sappey-Marinier Source Type: research
Heartbeat monitoring from adaptively down-sampled electrocardiogram
Heartbeats Holter monitoring is important for the detection of arrhythmias and possible anomalies, which are predictive of cardiovascular risks and infections. Reducing the number of acquired samples is useful to save energy and memory, but a proper down-sampling schedule is needed to record all useful information.MethodAn adaptive algorithm for the non-uniform down-sampling of data is used to reduce the mean sampling frequency of ECG data. The acquired data are processed to extract RR rhythm and to classify the heartbeats among a set of possible types of arrhythmias. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 29, 2017 Category: Bioinformatics Authors: Luca Mesin Source Type: research
Well-Balanced System for Coronary Calcium Detection and Volume Measurement in a Low Resolution Intravascular Ultrasound Videos
Accurate and fast quantitative assessment of calcium volume is required during the planning of percutaneous coronary interventions procedures. Low resolution in intravascular ultrasound (IVUS) coronary videos poses a threat to calcium detection causing over-estimation in volume measurement. We introduce a correction block that counter-balances the bias introduced during the calcium detection process. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 29, 2017 Category: Bioinformatics Authors: Sumit K. Banchhor, Narendra D. Londhe, Tadashi Araki, Luca Saba, Petia Radeva, John R. Laird, Jasjit S. Suri, Fellow AIMBE Source Type: research
Automatic Segmentation of Dermoscopy Images using Saliency Combined with Otsu Threshold
Segmentation is one of the crucial steps for the computer-aided diagnosis (CAD) of skin cancer with dermoscopy images. To accurately extract lesion borders from dermoscopy images, a novel automatic segmentation algorithm using saliency combined with Otsu threshold is proposed in this paper, which includes enhancement and segmentation stages. In the enhancement stage, prior information on healthy skin is extracted, and the color saliency map and brightness saliency map are constructed respectively. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 28, 2017 Category: Bioinformatics Authors: Haidi Fan, Fengying Xie, Yang Li, Zhiguo Jiang, Jie Liu Source Type: research
A Multi-Resolution Approach for Spinal Metastasis Detection using Deep Siamese Neural Networks
In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning methods. To accommodate the large variability in metastatic lesion sizes, we develop a Siamese deep neural network approach comprising three identical subnetworks for multi-resolution analysis and detection of spinal metastasis. At each location of interest, three image patches at three different resolutions are extracted and used as the input to the networks. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 25, 2017 Category: Bioinformatics Authors: Juan Wang, Zhiyuan Fang, Ning Lang, Huishu Yuan, Min-Ying Su, Pierre Baldi Source Type: research
Automatic media-adventitia IVUS image segmentation based on sparse representation framework and dynamic directional active contour model
Segmentation of the arterial wall boundaries from intravascular ultrasound images is an important image processing task in order to quantify arterial wall characteristics such as shape, area, thickness and eccentricity. Since manual segmentation of these boundaries is a laborious and time consuming procedure, many researchers attempted to develop (semi-) automatic segmentation techniques as a powerful tool for educational and clinical purposes in the past but as yet there is any clinically approved method in the market. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 24, 2017 Category: Bioinformatics Authors: Fahimeh Sadat Zakeri, Seyed Kamaledin Setarehdan, Somayye Norouzi Source Type: research
