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  1 / 23098 MEDLINE  
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[PMID]:29202689
[Au] Autor:Jalili V; Matteucci M; Masseroli M; Ceri S
[Ad] Endereço:Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, 20133, Italy. vahid.jalili@polimi.it.
[Ti] Título:Explorative visual analytics on interval-based genomic data and their metadata.
[So] Source:BMC Bioinformatics;18(1):536, 2017 Dec 04.
[Is] ISSN:1471-2105
[Cp] País de publicação:England
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: With the wide-spreading of public repositories of NGS processed data, the availability of user-friendly and effective tools for data exploration, analysis and visualization is becoming very relevant. These tools enable interactive analytics, an exploratory approach for the seamless "sense-making" of data through on-the-fly integration of analysis and visualization phases, suggested not only for evaluating processing results, but also for designing and adapting NGS data analysis pipelines. RESULTS: This paper presents abstractions for supporting the early analysis of NGS processed data and their implementation in an associated tool, named GenoMetric Space Explorer (GeMSE). This tool serves the needs of the GenoMetric Query Language, an innovative cloud-based system for computing complex queries over heterogeneous processed data. It can also be used starting from any text files in standard BED, BroadPeak, NarrowPeak, GTF, or general tab-delimited format, containing numerical features of genomic regions; metadata can be provided as text files in tab-delimited attribute-value format. GeMSE allows interactive analytics, consisting of on-the-fly cycling among steps of data exploration, analysis and visualization that help biologists and bioinformaticians in making sense of heterogeneous genomic datasets. By means of an explorative interaction support, users can trace past activities and quickly recover their results, seamlessly going backward and forward in the analysis steps and comparative visualizations of heatmaps. CONCLUSIONS: GeMSE effective application and practical usefulness is demonstrated through significant use cases of biological interest. GeMSE is available at http://www.bioinformatics.deib.polimi.it/GeMSE/ , and its source code is available at https://github.com/Genometric/GeMSE under GPLv3 open-source license.
[Mh] Termos MeSH primário: Bases de Dados Genéticas
Genômica/métodos
Metadados
[Mh] Termos MeSH secundário: Células A549
Dexametasona/farmacologia
Etanol/farmacologia
Seres Humanos
Modelos Teóricos
Reconhecimento Automatizado de Padrão
Mapeamento de Interação de Proteínas
Software
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Nm] Nome de substância:
3K9958V90M (Ethanol); 7S5I7G3JQL (Dexamethasone)
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180309
[Lr] Data última revisão:
180309
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:171206
[St] Status:MEDLINE
[do] DOI:10.1186/s12859-017-1945-9


  2 / 23098 MEDLINE  
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[PMID]:29480863
[Au] Autor:Wu TC; Wu KL; Hu WL; Sheen JM; Lu CN; Chiang JY; Hung YC
[Ad] Endereço:Department of Chinese Medicine.
[Ti] Título:Tongue diagnosis indices for upper gastrointestinal disorders: Protocol for a cross-sectional, case-controlled observational study.
[So] Source:Medicine (Baltimore);97(2):e9607, 2018 Jan.
[Is] ISSN:1536-5964
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:BACKGROUND: Upper gastrointestinal disorders are common in clinical practice, for example, gastritis, peptic ulcer disease, and gastroesophageal reflux disease. Panendoscopy or upper gastrointestinal endoscopy is viewed as the primary tool for examining the upper gastrointestinal mucosa, and permitting biopsy and endoscopic therapy. Although panendoscopy is considered to be a safe procedure with minimal complications, there are still some adverse effects, and patients are often anxious about undergoing invasive procedures. Traditional Chinese medicine tongue diagnosis plays an important role in differentiation of symptoms because the tongue reflects the physiological and pathological condition of the body. The automatic tongue diagnosis system (ATDS), which noninvasively captures tongue images, can provide objective and reliable diagnostic information. METHODS: This protocol is a cross-sectional, case-controlled observational study investigating the usefulness of the ATDS in clinical practice by examining its efficacy as a diagnostic tool for upper gastrointestinal disorders. Volunteers over 20 years old with and without upper gastrointestinal symptoms will be enrolled. Tongue images will be captured and the patients divided into 4 groups according to their panendoscopy reports, including a gastritis group, peptic ulcer disease group, gastroesophageal reflux disease group, and healthy group. Nine primary tongue features will be extracted and analyzed, including tongue shape, tongue color, tooth mark, tongue fissure, fur color, fur thickness, saliva, ecchymosis, and red dots. OBJECTIVES: The aim of this protocol is to apply a noninvasive ATDS to evaluate tongue manifestations of patients with upper gastrointestinal disorders and examine its efficacy as a diagnostic tool.
[Mh] Termos MeSH primário: Doenças do Sistema Digestório/diagnóstico
Medicina Tradicional Chinesa
Língua
[Mh] Termos MeSH secundário: Estudos de Casos e Controles
Estudos Transversais
Doenças do Sistema Digestório/patologia
Medicina Tradicional Chinesa/instrumentação
Medicina Tradicional Chinesa/métodos
Reconhecimento Automatizado de Padrão
Língua/patologia
[Pt] Tipo de publicação:JOURNAL ARTICLE; OBSERVATIONAL STUDY
[Em] Mês de entrada:1803
[Cu] Atualização por classe:180305
[Lr] Data última revisão:
180305
[Sb] Subgrupo de revista:AIM; IM
[Da] Data de entrada para processamento:180227
[St] Status:MEDLINE
[do] DOI:10.1097/MD.0000000000009607


  3 / 23098 MEDLINE  
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[PMID]:29346410
[Au] Autor:Cormode G; Dasgupta A; Goyal A; Lee CH
[Ad] Endereço:Department of Computer Science, University of Warwick, Coventry, United Kingdom.
[Ti] Título:An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs.
[So] Source:PLoS One;13(1):e0191175, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users' queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop). We identify several optimizations which improve performance, suitable for deployment in very large scale settings. The experimental results demonstrate our variants of LSH achieve the robust performance with better recall compared with "vanilla" LSH, even when using the same amount of space.
[Mh] Termos MeSH primário: Algoritmos
Ferramenta de Busca
[Mh] Termos MeSH secundário: Inteligência Artificial
Bases de Dados Factuais
Processamento de Imagem Assistida por Computador
Armazenamento e Recuperação da Informação
Internet
Processamento de Linguagem Natural
Reconhecimento Automatizado de Padrão
[Pt] Tipo de publicação:EVALUATION STUDIES; JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180220
[Lr] Data última revisão:
180220
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180119
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0191175


  4 / 23098 MEDLINE  
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[PMID]:27778099
[Au] Autor:Fiorenzato E; Weis L; Seppi K; Onofrj M; Cortelli P; Zanigni S; Tonon C; Kaufmann H; Shepherd TM; Poewe W; Krismer F; Wenning G; Antonini A; Biundo R; Movement Disorders Society MSA (MODIMSA) Neuropsychology and Imaging Study Groups
[Ad] Endereço:Parkinson Disease and Movement Disorders Unit, IRCCS San Camillo Hospital Foundation, via Alberoni, 70, 30126, Venice-Lido, Italy. eleonora.fiorenzato@gmail.com.
[Ti] Título:Brain structural profile of multiple system atrophy patients with cognitive impairment.
[So] Source:J Neural Transm (Vienna);124(3):293-302, 2017 Mar.
[Is] ISSN:1435-1463
[Cp] País de publicação:Austria
[La] Idioma:eng
[Ab] Resumo:Current consensus diagnostic criteria for multiple system atrophy (MSA) consider dementia a non-supporting feature, although cognitive impairment and even frank dementia are reported in clinical practice. Mini-Mental State Examination (MMSE) is a commonly used global cognitive scale, and in a previous study, we established an MSA-specific screening cut-off score <27 to identify cognitive impairment. Finally, MSA neuroimaging findings suggest the presence of structural alterations in patients with cognitive deficits, although the extent of the anatomical changes is unclear. The aim of our multicenter study is to better characterize anatomical changes associated with cognitive impairment in MSA and to further investigate cortical and subcortical structural differences versus healthy controls (HC). We examined retrospectively 72 probable MSA patients [50 with normal cognition (MSA-NC) and 22 cognitively impaired (MSA-CI) based on MMSE <27] and compared them to 36 HC using gray- and white-matter voxel-based morphometry and fully automated subcortical segmentation. Compared to HC, MSA patients showed widespread cortical (bilateral frontal, occipito-temporal, and parietal areas), subcortical, and white-matter alterations. However, MSA-CI showed only focal volume reduction in the left dorsolateral prefrontal cortex compared with MSA-NC. These results suggest only a marginal contribution of cortical pathology to cognitive deficits. We believe that cognitive dysfunction is driven by focal fronto-striatal degeneration in line with the concept of "subcortical cognitive impairment".
[Mh] Termos MeSH primário: Encéfalo/diagnóstico por imagem
Disfunção Cognitiva/complicações
Disfunção Cognitiva/diagnóstico por imagem
Atrofia de Múltiplos Sistemas/complicações
Atrofia de Múltiplos Sistemas/diagnóstico por imagem
[Mh] Termos MeSH secundário: Feminino
Substância Cinzenta/diagnóstico por imagem
Seres Humanos
Imagem Tridimensional
Imagem por Ressonância Magnética
Masculino
Testes de Estado Mental e Demência
Meia-Idade
Atrofia de Múltiplos Sistemas/psicologia
Neuroimagem
Tamanho do Órgão
Reconhecimento Automatizado de Padrão
Estudos Retrospectivos
Substância Branca/diagnóstico por imagem
[Pt] Tipo de publicação:JOURNAL ARTICLE; MULTICENTER STUDY
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180220
[Lr] Data última revisão:
180220
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161026
[St] Status:MEDLINE
[do] DOI:10.1007/s00702-016-1636-0


  5 / 23098 MEDLINE  
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[PMID]:28466009
[Au] Autor:Han G; Liu X; Soomro NQ; Sun J; Zhao Y; Zhao X; Zhou C
[Ad] Endereço:Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
[Ti] Título:Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT.
[So] Source:Biomed Res Int;2017:3842659, 2017.
[Is] ISSN:2314-6141
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Computer-aided detection (CAD) of lobulation can help radiologists to diagnose/detect lung diseases easily and accurately. Compared to CAD of nodule and other lung lesions, CAD of lobulation remained an unexplored problem due to very complex and varying nature of lobulation. Thus, many state-of-the-art methods could not detect successfully. Hence, we revisited classical methods with the capability of extracting undulated characteristics and designed a sliding window based framework for lobulation detection in this paper. Under the designed framework, we investigated three categories of lobulation classification algorithms: template matching, feature based classifier, and bending energy. The resultant detection algorithms were evaluated through experiments on LISS database. The experimental results show that the algorithm based on combination of global context feature and BOF encoding has best overall performance, resulting in 1 score of 0.1009. Furthermore, bending energy method is shown to be appropriate for reducing false positives. We performed bending energy method following the LIOP-LBP mixture feature, the average positive detection per image was reduced from 30 to 22, and 1 score increased to 0.0643 from 0.0599. To the best of our knowledge this is the first kind of work for direct lobulation detection and first application of bending energy to any kind of lobulation work.
[Mh] Termos MeSH primário: Neoplasias Pulmonares/diagnóstico por imagem
Pulmão/diagnóstico por imagem
Nódulo Pulmonar Solitário/diagnóstico por imagem
Tomografia Computadorizada por Raios X/métodos
[Mh] Termos MeSH secundário: Seres Humanos
Pulmão/fisiopatologia
Neoplasias Pulmonares/diagnóstico
Neoplasias Pulmonares/patologia
Reconhecimento Automatizado de Padrão
Intensificação de Imagem Radiográfica
Interpretação de Imagem Radiográfica Assistida por Computador
Nódulo Pulmonar Solitário/diagnóstico
Tórax/diagnóstico por imagem
Tórax/patologia
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180216
[Lr] Data última revisão:
180216
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170504
[St] Status:MEDLINE
[do] DOI:10.1155/2017/3842659


  6 / 23098 MEDLINE  
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[PMID]:28466003
[Au] Autor:Yang G; Hu Z
[Ad] Endereço:School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China.
[Ti] Título:Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation.
[So] Source:Biomed Res Int;2017:1096028, 2017.
[Is] ISSN:2314-6141
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the original data effectively and improve the final clustering accuracy while segmenting the subspace. The introduction of nonnegative constraints makes the computation with some sparsity, which enhances the robustness of the algorithm. Different from Lat-LRR, a new solution model is adopted to simplify the computational complexity. The experimental results show that the proposed algorithm has good feature extraction performance for the heavy redundancy and noise gene expression profile, which, compared with LRR and Lat-LRR, can achieve better clustering accuracy.
[Mh] Termos MeSH primário: Perfilação da Expressão Gênica/estatística & dados numéricos
Reconhecimento Automatizado de Padrão
Transcriptoma/genética
[Mh] Termos MeSH secundário: Algoritmos
Análise por Conglomerados
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180216
[Lr] Data última revisão:
180216
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:170504
[St] Status:MEDLINE
[do] DOI:10.1155/2017/1096028


  7 / 23098 MEDLINE  
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[PMID]:29373580
[Au] Autor:Chen S; Liu T; Shu Z; Xin S; He Y; Tu C
[Ad] Endereço:Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China.
[Ti] Título:Fast and robust shape diameter function.
[So] Source:PLoS One;13(1):e0190666, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:The shape diameter function (SDF) is a scalar function defined on a closed manifold surface, measuring the neighborhood diameter of the object at each point. Due to its pose oblivious property, SDF is widely used in shape analysis, segmentation and retrieval. However, computing SDF is computationally expensive since one has to place an inverted cone at each point and then average the penetration distances for a number of rays inside the cone. Furthermore, the shape diameters are highly sensitive to local geometric features as well as the normal vectors, hence diminishing their applications to real-world meshes which often contain rich geometric details and/or various types of defects, such as noise and gaps. In order to increase the robustness of SDF and promote it to a wide range of 3D models, we define SDF by offsetting the input object a little bit. This seemingly minor change brings three significant benefits: First, it allows us to compute SDF in a robust manner since the offset surface is able to give reliable normal vectors. Second, it runs many times faster since at each point we only need to compute the penetration distance along a single direction, rather than tens of directions. Third, our method does not require watertight surfaces as the input-it supports both point clouds and meshes with noise and gaps. Extensive experimental results show that the offset-surface based SDF is robust to noise and insensitive to geometric details, and it also runs about 10 times faster than the existing method. We also exhibit its usefulness using two typical applications including shape retrieval and shape segmentation, and observe a significant improvement over the existing SDF.
[Mh] Termos MeSH primário: Imagem Tridimensional/métodos
Reconhecimento Automatizado de Padrão/métodos
[Mh] Termos MeSH secundário: Algoritmos
Simulação por Computador
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180213
[Lr] Data última revisão:
180213
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180127
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0190666


  8 / 23098 MEDLINE  
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[PMID]:29304130
[Au] Autor:Giacomini G; Pavan ALM; Altemani JMC; Duarte SB; Fortaleza CMCB; Miranda JRA; de Pina DR
[Ad] Endereço:Instituto de Biociências de Botucatu, Universidade Estadual Paulista (IBB-UNESP), Botucatu, São Paulo, Brazil.
[Ti] Título:Computed tomography-based volumetric tool for standardized measurement of the maxillary sinus.
[So] Source:PLoS One;13(1):e0190770, 2018.
[Is] ISSN:1932-6203
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:Volume measurements of maxillary sinus may be useful to identify diseases affecting paranasal sinuses. However, literature shows a lack of consensus in studies measuring the volume. This may be attributable to different computed tomography data acquisition techniques, segmentation methods, focuses of investigation, among other reasons. Furthermore, methods for volumetrically quantifying the maxillary sinus are commonly manual or semiautomated, which require substantial user expertise and are time-consuming. The purpose of the present study was to develop an automated tool for quantifying the total and air-free volume of the maxillary sinus based on computed tomography images. The quantification tool seeks to standardize maxillary sinus volume measurements, thus allowing better comparisons and determinations of factors that influence maxillary sinus size. The automated tool utilized image processing techniques (watershed, threshold, and morphological operators). The maxillary sinus volume was quantified in 30 patients. To evaluate the accuracy of the automated tool, the results were compared with manual segmentation that was performed by an experienced radiologist using a standard procedure. The mean percent differences between the automated and manual methods were 7.19% ± 5.83% and 6.93% ± 4.29% for total and air-free maxillary sinus volume, respectively. Linear regression and Bland-Altman statistics showed good agreement and low dispersion between both methods. The present automated tool for maxillary sinus volume assessment was rapid, reliable, robust, accurate, and reproducible and may be applied in clinical practice. The tool may be used to standardize measurements of maxillary volume. Such standardization is extremely important for allowing comparisons between studies, providing a better understanding of the role of the maxillary sinus, and determining the factors that influence maxillary sinus size under normal and pathological conditions.
[Mh] Termos MeSH primário: Tomografia Computadorizada de Feixe Cônico/métodos
Seio Maxilar/diagnóstico por imagem
Reconhecimento Automatizado de Padrão/métodos
[Mh] Termos MeSH secundário: Adulto
Algoritmos
Feminino
Seres Humanos
Imagem Tridimensional/métodos
Modelos Lineares
Masculino
Tamanho do Órgão
Reprodutibilidade dos Testes
Estudos Retrospectivos
[Pt] Tipo de publicação:JOURNAL ARTICLE; RESEARCH SUPPORT, NON-U.S. GOV'T; VALIDATION STUDIES
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180210
[Lr] Data última revisão:
180210
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:180106
[St] Status:MEDLINE
[do] DOI:10.1371/journal.pone.0190770


  9 / 23098 MEDLINE  
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[PMID]:27774876
[Au] Autor:Lu S; Qiu X; Shi J; Li N; Lu ZH; Chen P; Yang MM; Liu FY; Jia WJ; Zhang Y
[Ti] Título:A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.
[So] Source:CNS Neurol Disord Drug Targets;16(1):23-29, 2017.
[Is] ISSN:1996-3181
[Cp] País de publicação:United Arab Emirates
[La] Idioma:eng
[Ab] Resumo:AIM: It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. METHOD: Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. RESULT: The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. CONCLUSION: The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection.
[Mh] Termos MeSH primário: Algoritmos
Encefalopatias/diagnóstico por imagem
Encéfalo/diagnóstico por imagem
Aprendizado de Máquina
[Mh] Termos MeSH secundário: Automação
Encéfalo/patologia
Encefalopatias/patologia
Entropia
Seres Humanos
Processamento de Imagem Assistida por Computador
Imagem Tridimensional
Imagem por Ressonância Magnética
Neuroimagem
Reconhecimento Automatizado de Padrão
Reprodutibilidade dos Testes
Análise de Ondaletas
[Pt] Tipo de publicação:JOURNAL ARTICLE
[Em] Mês de entrada:1802
[Cu] Atualização por classe:180209
[Lr] Data última revisão:
180209
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161025
[St] Status:MEDLINE
[do] DOI:10.2174/1871527315666161019153259


  10 / 23098 MEDLINE  
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[PMID]:27775509
[Au] Autor:Hussain MA; Bhuiyan A; Turpin A; Luu CD; Smith RT; Guymer RH; Kotagiri R
[Ti] Título:Automatic Identification of Pathology-Distorted Retinal Layer Boundaries Using SD-OCT Imaging.
[So] Source:IEEE Trans Biomed Eng;64(7):1638-1649, 2017 07.
[Is] ISSN:1558-2531
[Cp] País de publicação:United States
[La] Idioma:eng
[Ab] Resumo:OBJECTIVE: We propose an effective automatic method for identification of four retinal layer boundaries from the spectral domain optical coherence tomography images in the presence and absence of pathologies and morphological changes due to disease. METHODS: The approach first finds an approximate location of three reference layers and then uses these to bound the search space for the actual layers, which is achieved by modeling the problem as a graph and applying Dijkstra's shortest path algorithm. The edge weight between nodes is determined using pixel distance, slope similarity to a reference, and nonassociativity of the layers, which is designed to overcome the distorting effects that pathology can play in the boundary determination. RESULTS: The accuracy of our method was evaluated on three different datasets. It outperforms the current five state-of-the-art methods. On average, the mean and standard deviation of the root-mean-square error in the form of mean ± standard deviation in pixels for our method is 1.57 ± 0.69, which is lower than compared to the existing top five methods of 16.17 ± 22.64, 6.66 ± 9.11, 5.70 ± 10.54, 3.69 ± 2.04, and 2.29 ± 1.54. CONCLUSION: Our method is highly accurate, robust, reliable, and consistent. This identification can enable to quantify the biomarkers of the retina in large-scale study for assessing, monitoring disease progression, as well as early detection of retinal diseases. SIGNIFICANCE: Identification of these boundaries can help to determine the loss of neuroretinal cells or layers and the presence of retinal pathology, which can be used as features for the automatic determination of the stages of retinal diseases.
[Mh] Termos MeSH primário: Interpretação de Imagem Assistida por Computador/métodos
Reconhecimento Automatizado de Padrão/métodos
Retina/patologia
Doenças Retinianas/diagnóstico por imagem
Doenças Retinianas/patologia
Tomografia de Coerência Óptica/métodos
[Mh] Termos MeSH secundário: Seres Humanos
Aprendizado de Máquina
Reprodutibilidade dos Testes
Sensibilidade e Especificidade
[Pt] Tipo de publicação:COMPARATIVE STUDY; EVALUATION STUDIES; JOURNAL ARTICLE; VALIDATION STUDIES
[Em] Mês de entrada:1710
[Cu] Atualização por classe:180205
[Lr] Data última revisão:
180205
[Sb] Subgrupo de revista:IM
[Da] Data de entrada para processamento:161025
[St] Status:MEDLINE
[do] DOI:10.1109/TBME.2016.2619120



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