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1.
Methods Mol Biol ; 2217: 27-37, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33215374

RESUMO

Focal adhesions in planar substrates constitute an excellent cellular resource to evaluate different parameters related to cell morphology, cytoskeletal organization, and adhesive strength. However, their intrinsic heterogeneity in terms of size, molecular composition, orientation, and so on complicates their analysis. Here, we describe a simple and straightforward ImageJ/Fiji-based method to quantify several parameters that describe the morphology and relative composition of focal adhesions. This type of analysis can be implemented in various ways and become useful for drug and shRNA screenings.


Assuntos
Citoesqueleto de Actina/ultraestrutura , Matriz Extracelular/ultraestrutura , Adesões Focais/ultraestrutura , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Imagem Molecular/métodos , Citoesqueleto de Actina/metabolismo , Actinas/química , Actinas/metabolismo , Animais , Células CHO , Adesão Celular , Linhagem Celular Tumoral , Cricetulus , Matriz Extracelular/metabolismo , Fibronectinas/química , Fibronectinas/metabolismo , Adesões Focais/metabolismo , Humanos , Camundongos , Células NIH 3T3 , Osteoblastos/metabolismo , Osteoblastos/ultraestrutura , Faloidina/química
2.
Bioessays ; 43(3): e2000257, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33377226

RESUMO

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents challenges that demand immediate attention. Here, we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA, and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay that complements the portfolio of SARS-CoV-2 serological assays. Sensitive, specific and quantitative serological assays are urgently needed for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.


Assuntos
Anticorpos Antivirais/sangue , Imunoensaio , Imunoglobulina A/sangue , Imunoglobulina G/sangue , Imunoglobulina M/sangue , Microscopia/métodos , /imunologia , /imunologia , /métodos , Imunofluorescência , Ensaios de Triagem em Larga Escala , Humanos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Soros Imunes/química , Aprendizado de Máquina , Sensibilidade e Especificidade
3.
PLoS One ; 15(12): e0236495, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33382698

RESUMO

The fruit fly Drosophila melanogaster is an important model organism for neuroscience with a wide array of genetic tools that enable the mapping of individual neurons and neural subtypes. Brain templates are essential for comparative biological studies because they enable analyzing many individuals in a common reference space. Several central brain templates exist for Drosophila, but every one is either biased, uses sub-optimal tissue preparation, is imaged at low resolution, or does not account for artifacts. No publicly available Drosophila ventral nerve cord template currently exists. In this work, we created high-resolution templates of the Drosophila brain and ventral nerve cord using the best-available technologies for imaging, artifact correction, stitching, and template construction using groupwise registration. We evaluated our central brain template against the four most competitive, publicly available brain templates and demonstrate that ours enables more accurate registration with fewer local deformations in shorter time.


Assuntos
Encéfalo/anatomia & histologia , Drosophila melanogaster/anatomia & histologia , Tecido Nervoso/anatomia & histologia , Neurônios/ultraestrutura , Animais , Encéfalo/ultraestrutura , Drosophila melanogaster/ultraestrutura , Feminino , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Masculino , Microscopia Confocal , Microscopia Eletrônica , Tecido Nervoso/ultraestrutura
4.
PLoS One ; 15(10): e0237570, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33044975

RESUMO

Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.


Assuntos
Nadadeiras de Animais/anatomia & histologia , Balaenoptera/anatomia & histologia , Aplicativos Móveis , Fotografação/métodos , Algoritmos , Animais , Bases de Dados Factuais , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Reconhecimento Automatizado de Padrão/métodos , Reconhecimento Automatizado de Padrão/estatística & dados numéricos , Fotografação/estatística & dados numéricos
5.
Ann Biol Clin (Paris) ; 78(5): 519-526, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-33026347

RESUMO

Digital morphology hematology analyzers are becoming more prevalent in laboratories Aims: investigate practices and assess the benefits and limits of digital automated microscopy in hematology. METHODS: questionnaire sent by e-mail in 2018 to French public and private laboratories. RESULTS: out of 118 responses (56 private, 62 public), 117 participants had a CellaVision® microscope, 1 had a West Medica®. Practices were sometimes different, especially in the choice of smears to be digitized or for quality controls (16.1% had internal quality controls, 48.3% external quality controls); 62.1% never used the red blood cell (RBC) characterization tool; the number of cells counted varied from 100 to 400. The study reported a high rate of agreement for these benefits: traceability (95.7%), staff training (94.1%), eye strain (91.4%), risk of error (87.2%), time saving (83.6%). Among the disadvantages, apart from the inadequate search for platelets clumps (93.2%), the agreement rates were often lower: adaptation to digital images (61.2%), difficult assessment of atypical morphologies (49.6%) or RBC morphology (49.6%). CONCLUSION: despite well-established benefits, standardization of practices and technical improvement are still needed.


Assuntos
Automação Laboratorial , Testes Hematológicos/instrumentação , Hematologia/instrumentação , Processamento de Imagem Assistida por Computador , Microscopia/instrumentação , Atitude do Pessoal de Saúde , Automação Laboratorial/instrumentação , Automação Laboratorial/métodos , Automação Laboratorial/estatística & dados numéricos , Computadores , Testes Diagnósticos de Rotina/instrumentação , Testes Diagnósticos de Rotina/métodos , Testes Diagnósticos de Rotina/estatística & dados numéricos , Testes Diagnósticos de Rotina/tendências , França/epidemiologia , Testes Hematológicos/métodos , Testes Hematológicos/estatística & dados numéricos , Testes Hematológicos/tendências , Hematologia/métodos , Hematologia/estatística & dados numéricos , Hematologia/tendências , Humanos , Processamento de Imagem Assistida por Computador/instrumentação , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Processamento de Imagem Assistida por Computador/tendências , Satisfação no Emprego , Microscopia/métodos , Microscopia/estatística & dados numéricos , Microscopia/tendências , Prática Profissional/estatística & dados numéricos , Prática Profissional/tendências , Controle de Qualidade , Inquéritos e Questionários
6.
PLoS One ; 15(10): e0239591, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33017406

RESUMO

Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.


Assuntos
Oryza/crescimento & desenvolvimento , Tecnologia de Sensoriamento Remoto/métodos , Algoritmos , Biomassa , Colômbia , Produtos Agrícolas/crescimento & desenvolvimento , Sistemas de Informação Geográfica/instrumentação , Sistemas de Informação Geográfica/estatística & dados numéricos , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Raios Infravermelhos , Aprendizado de Máquina , Tecnologia de Sensoriamento Remoto/instrumentação , Tecnologia de Sensoriamento Remoto/estatística & dados numéricos , Análise Espaço-Temporal
7.
Sensors (Basel) ; 20(18)2020 Sep 12.
Artigo em Inglês | MEDLINE | ID: mdl-32932585

RESUMO

The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load.


Assuntos
Inteligência Artificial , Betacoronavirus , Cegueira/reabilitação , Infecções por Coronavirus/prevenção & controle , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Auxiliares Sensoriais , Dispositivos Eletrônicos Vestíveis , Acústica , Adulto , Algoritmos , Inteligência Artificial/estatística & dados numéricos , Cegueira/psicologia , Visão de Cores , Sistemas Computacionais/estatística & dados numéricos , Infecções por Coronavirus/epidemiologia , Desenho de Equipamento , Feminino , Alemanha/epidemiologia , Humanos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Masculino , Pneumonia Viral/epidemiologia , Robótica , Semântica , Óculos Inteligentes/estatística & dados numéricos , Pessoas com Deficiência Visual/reabilitação , Dispositivos Eletrônicos Vestíveis/estatística & dados numéricos
8.
Sensors (Basel) ; 20(18)2020 Sep 14.
Artigo em Inglês | MEDLINE | ID: mdl-32937867

RESUMO

The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has resulted in a global pandemic. Correct facemask wearing is valuable for infectious disease control, but the effectiveness of facemasks has been diminished, mostly due to improper wearing. However, there have not been any published reports on the automatic identification of facemask-wearing conditions. In this study, we develop a new facemask-wearing condition identification method by combining image super-resolution and classification networks (SRCNet), which quantifies a three-category classification problem based on unconstrained 2D facial images. The proposed algorithm contains four main steps: Image pre-processing, facial detection and cropping, image super-resolution, and facemask-wearing condition identification. Our method was trained and evaluated on the public dataset Medical Masks Dataset containing 3835 images with 671 images of no facemask-wearing, 134 images of incorrect facemask-wearing, and 3030 images of correct facemask-wearing. Finally, the proposed SRCNet achieved 98.70% accuracy and outperformed traditional end-to-end image classification methods using deep learning without image super-resolution by over 1.5% in kappa. Our findings indicate that the proposed SRCNet can achieve high-accuracy identification of facemask-wearing conditions, thus having potential applications in epidemic prevention involving COVID-19.


Assuntos
Betacoronavirus , Infecções por Coronavirus/prevenção & controle , Máscaras , Pandemias/prevenção & controle , Pneumonia Viral/prevenção & controle , Algoritmos , China/epidemiologia , Infecções por Coronavirus/epidemiologia , Bases de Dados Factuais , Aprendizado Profundo , Face , Humanos , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Máscaras/classificação , Máscaras/estatística & dados numéricos , Redes Neurais de Computação , Pneumonia Viral/epidemiologia
9.
PLoS One ; 15(8): e0236493, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32745102

RESUMO

Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance. In particular, the U-net architecture has been widely used for segmentation in various biomedical related fields. In this paper, we propose a patch-wise U-net architecture for the automatic segmentation of brain structures in structural MRI. In the proposed brain segmentation method, the non-overlapping patch-wise U-net is used to overcome the drawbacks of conventional U-net with more retention of local information. In our proposed method, the slices from an MRI scan are divided into non-overlapping patches that are fed into the U-net model along with their corresponding patches of ground truth so as to train the network. The experimental results show that the proposed patch-wise U-net model achieves a Dice similarity coefficient (DSC) score of 0.93 in average and outperforms the conventional U-net and the SegNet-based methods by 3% and 10%, respectively, for on Open Access Series of Imaging Studies (OASIS) and Internet Brain Segmentation Repository (IBSR) dataset.


Assuntos
Encéfalo/diagnóstico por imagem , Aprendizado Profundo , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Imagem por Ressonância Magnética/estatística & dados numéricos , Algoritmos , Humanos , Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Redes Neurais de Computação
10.
PLoS One ; 15(5): e0233028, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32407341

RESUMO

Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross-sections. We acquired serial median nerve cross-sections from human cadaveric samples, staining one set with hematoxylin and eosin (H&E) and the other using immunohistochemistry (IHC) with anti-neurofilament antibody. We developed a four-step processing pipeline involving registration, fascicle detection, segmentation, and reconstruction. We compared the output of each step to manual ground truths, and additionally compared the final models to commonly used extrusions, via intersection-over-union (IOU). Fascicle detection and segmentation required the use of a neural network and active contours in H&E-stained images, but only simple image processing methods for IHC-stained images. Reconstruction achieved an IOU of 0.42±0.07 for H&E and 0.37±0.16 for IHC images, with errors partially attributable to global misalignment at the registration step, rather than poor reconstruction. This work provides a quantitative baseline for fully automatic construction of peripheral nerve models. Our models provided fascicular shape and branching information that would be lost via extrusion.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Nervos Periféricos/anatomia & histologia , Cadáver , Humanos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Imageamento Tridimensional/estatística & dados numéricos , Imuno-Histoquímica , Modelos Anatômicos , Modelos Neurológicos , Próteses Neurais , Coloração e Rotulagem
11.
PLoS One ; 15(5): e0232403, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32374774

RESUMO

We present novel multi-energy X-ray imaging methods for direct radiography and computed tomography. The goal is to determine the contribution of thickness, mass density and atomic composition to the measured X-ray absorption in the sample. Algorithms have been developed by our own to calculate new X-ray images using data from an unlimited amount of scans/images of different tube voltages by pixelwise fitting of the detected gray levels. The resulting images then show a contrast that is influenced either by the atomic number of the elements in the sample (photoelectric interactions) or by the mass density (Compton scattering). For better visualization, those images can be combined to a color image where different materials can easily be distinguished. In the case of computed tomography no established true multi-energy methodology that does not require an energy sensitive detector is known to the authors. The existing dual-energy methods often yield noisy results that need spatial averaging for clear interpretation. The goal of the method presented here is to qualitatively calculate atomic number and mass density images without loosing resolution while reducing the noise by the use of more than two X-ray energies. The resulting images are generated without the need of calibration stan-dards in an automatic and fast data processing routine. They provide additional information that might be of special interest in cases like archaeology where the destruction of a sample to determine its composition is no option, but a increase in measurement time is of little concern.


Assuntos
Radiografia/métodos , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Simulação por Computador , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Imageamento Tridimensional/métodos , Imageamento Tridimensional/estatística & dados numéricos , Ciência dos Materiais , Minerais/química , Radiografia/estatística & dados numéricos , Espalhamento de Radiação , Tomografia Computadorizada por Raios X/estatística & dados numéricos , Raios X
12.
PLoS One ; 15(5): e0232433, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32459811

RESUMO

In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Incêndios Florestais/prevenção & controle , China , Computação em Nuvem , Sistemas Computacionais , Conservação dos Recursos Naturais/métodos , Conservação dos Recursos Naturais/estatística & dados numéricos , Análise Discriminante , Fenômenos Geológicos , Humanos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Software , Propriedades de Superfície , Incêndios Florestais/estatística & dados numéricos
13.
PLoS One ; 15(4): e0230997, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32298280

RESUMO

The existing tamper detection schemes for absolute moment block truncation coding (AMBTC) compressed images are able to detect the tampering. However, the marked image qualities of these schemes can be enhanced, and their authentication methods may fail to detect some special tampering. We propose a secure AMBTC tamper detection scheme that preserves high image fidelity with excellent detectability. In the proposed approach, a bit in bitmaps of AMBTC codes is sequentially toggled to generate a set of authentication codes. The one that causes the least distortion is embedded into the quantization levels with the guidance of a key-generated reference table (RT). Without the correct key, the same reference table cannot be constructed. Therefore, the proposed method is able to detect various kinds of malicious tampering, including those special tampering techniques designed for RT-based authentication schemes. The proposed method not only offers better image quality, but also provides an excellent and satisfactory detectability as compared with previous works.


Assuntos
Processamento de Imagem Assistida por Computador , Medidas de Segurança , Algoritmos , Segurança Computacional/normas , Segurança Computacional/estatística & dados numéricos , Compressão de Dados/normas , Compressão de Dados/estatística & dados numéricos , Humanos , Processamento de Imagem Assistida por Computador/normas , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Internet/normas , Internet/estatística & dados numéricos , Medidas de Segurança/normas , Medidas de Segurança/estatística & dados numéricos
14.
PLoS One ; 15(3): e0229651, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32126113

RESUMO

Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image's brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a new thresholding method is proposed in this paper. The proposed method constructs a new two dimensional histogram using brightness of a pixel and local relative entropy of it's neighbor pixels. The local relative entropy (LRE) measures the brightness difference between a pixel and it's neighbor pixels. The two dimensional histogram, consisting of gray level and LRE, can reflect the contextual information between pixels to a certain extent. The optimal thresholding vector is obtained via minimizing cross entropy criteria. Experimental results show that the proposed method can achieve more accurate segmentation results than other thresholding methods.


Assuntos
Inteligência Artificial , Processamento de Imagem Assistida por Computador/métodos , Algoritmos , Inteligência Artificial/estatística & dados numéricos , Cor , Entropia , Humanos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Conceitos Matemáticos
15.
PLoS One ; 15(3): e0229560, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32176698

RESUMO

PURPOSE: Image texture is increasingly used to discriminate tissues and lesions in PET/CT. For quantification or in computer-aided diagnosis, textural feature analysis must produce robust and comparable values. Because statistical feature values depend on image count statistics, we investigated in depth the stability of Haralick features values as functions of acquisition duration, and for common image resolutions and reconstructions. METHODS: A homogeneous cylindrical phantom containing 9.6 kBq/ml Ge-68 was repeatedly imaged on a Siemens Biograph mCT, with acquisition durations ranging from three seconds to three hours. Images with 1.5, 2, and 4 mm isometrically spaced voxels were reconstructed with filtered back-projection (FBP), ordered subset expectation maximization (OSEM), and the Siemens TrueX algorithm. We analysed Haralick features derived from differently quantized (3 to 8-bit) grey level co-occurrence matrices (GLCMs) as functions of exposure E, which we defined as the product of activity concentration in a volume of interest (VOI) and acquisition duration. The VOI was a 50 mm wide cube at the centre of the phantom. Feature stability was defined for df/dE → 0. RESULTS: The most stable feature values occurred in low resolution FBPs, whereas some feature values from 1.5 mm TrueX reconstructions ranged over two orders of magnitude. Within the same reconstructions, most feature value-exposure curves reached stable plateaus at similar exposures, regardless of GLCM quantization. With 8-bit GLCM, median time to stability was 16 s and 22 s for FBPs, 18 s and 125 s for OSEM, and 23 s, 45 s, and 76 s for PSF reconstructions, with longer durations for higher resolutions. Stable exposures coincided in OSEM and TrueX reconstructions with image noise distributions converging to a Gaussian. In FBP, the occurrence of stable values coincided the disappearance of negatives image values in the VOI. CONCLUSIONS: Haralick feature values depend strongly on exposure, but invariance exists within defined domains of exposure. Here, we present an easily replicable procedure to identify said stable exposure domains, where image noise does not substantially add to textural feature values. Only by imaging at predetermined feature-invariant exposure levels and by adjusting exposure to expected activity concentrations, can textural features have a quantitative use in PET/CT. The necessary exposure levels are attainable by modern PET/CT systems in clinical routine.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons/métodos , Algoritmos , Animais , Fluordesoxiglucose F18 , Humanos , Imagens de Fantasmas/estatística & dados numéricos , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons/normas , Tomografia Computadorizada com Tomografia por Emissão de Pósitrons/estatística & dados numéricos , Tomografia por Emissão de Pósitrons/métodos , Compostos Radiofarmacêuticos
16.
PLoS One ; 15(3): e0229526, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32150547

RESUMO

In diffusion MRI, the Ensemble Average diffusion Propagator (EAP) provides relevant micro-structural information and meaningful descriptive maps of the white matter previously obscured by traditional techniques like Diffusion Tensor Imaging (DTI). The direct estimation of the EAP, however, requires a dense sampling of the Cartesian q-space involving a huge amount of samples (diffusion gradients) for proper reconstruction. A collection of more efficient techniques have been proposed in the last decade based on parametric representations of the EAP, but they still imply acquiring a large number of diffusion gradients with different b-values (shells). Paradoxically, this has come together with an effort to find scalar measures gathering all the q-space micro-structural information probed in one single index or set of indices. Among them, the return-to-origin (RTOP), return-to-plane (RTPP), and return-to-axis (RTAP) probabilities have rapidly gained popularity. In this work, we propose the so-called "Apparent Measures Using Reduced Acquisitions" (AMURA) aimed at computing scalar indices that can mimic the sensitivity of state of the art EAP-based measures to micro-structural changes. AMURA drastically reduces both the number of samples needed and the computational complexity of the estimation of diffusion properties by assuming the diffusion anisotropy is roughly independent from the radial direction. This simplification allows us to compute closed-form expressions from single-shell information, so that AMURA remains compatible with standard acquisition protocols commonly used even in clinical practice. Additionally, the analytical form of AMURA-based measures, as opposed to the iterative, non-linear reconstruction ubiquitous to full EAP techniques, turns the newly introduced apparent RTOP, RTPP, and RTAP both robust and efficient to compute.


Assuntos
Imagem de Difusão por Ressonância Magnética/métodos , Interpretação de Imagem Assistida por Computador/métodos , Interpretação de Imagem Assistida por Computador/estatística & dados numéricos , Algoritmos , Encéfalo/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética/estatística & dados numéricos , Imagem de Tensor de Difusão/métodos , Aumento da Imagem/métodos , Processamento de Imagem Assistida por Computador/métodos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Imagem por Ressonância Magnética/métodos , Substância Branca/diagnóstico por imagem
17.
Nat Commun ; 11(1): 872, 2020 02 13.
Artigo em Inglês | MEDLINE | ID: mdl-32054847

RESUMO

Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, how sparsely active neurons reliably represent complex natural images and how the information is optimally decoded from these representations have not been revealed. Using two-photon calcium imaging, we recorded visual responses to natural images from several hundred V1 neurons and reconstructed the images from neural activity in anesthetized and awake mice. A single natural image is linearly decodable from a surprisingly small number of highly responsive neurons, and the remaining neurons even degrade the decoding. Furthermore, these neurons reliably represent the image across trials, regardless of trial-to-trial response variability. Based on our results, diverse, partially overlapping receptive fields ensure sparse and reliable representation. We suggest that information is reliably represented while the corresponding neuronal patterns change across trials and collecting only the activity of highly responsive neurons is an optimal decoding strategy for the downstream neurons.


Assuntos
Células Receptoras Sensoriais/fisiologia , Córtex Visual/citologia , Córtex Visual/fisiologia , Percepção Visual/fisiologia , Animais , Feminino , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Microscopia de Fluorescência por Excitação Multifotônica , Reconhecimento Visual de Modelos/fisiologia , Estimulação Luminosa
18.
Alzheimers Dement ; 16(1): 192-199, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31914223

RESUMO

INTRODUCTION: Mild behavioral impairment (MBI) is characterized by the emergence of neuropsychiatric symptoms in elderly persons. Here, we examine the associations between MBI and Alzheimer's disease (AD) biomarkers in asymptomatic elderly individuals. METHODS: Ninety-six cognitively normal elderly individuals underwent MRI, [18 F]AZD4694 ß-amyloid-PET, and [18 F]MK6240 tau-PET. MBI was assessed using the MBI Checklist (MBI-C). Pearson's correlations and voxel-based regressions were used to evaluate the relationship between MBI-C score and [18 F]AZD4694 retention, [18 F]MK6240 retention, and gray matter (GM) volume. RESULTS: Pearson correlations revealed a positive relationship between MBI-C score and global and striatal [18 F]AZD4694 standardized uptake value ratios (SUVRs). Voxel-based regression analyses revealed a positive correlation between MBI-C score and [18 F]AZD4694 retention. No significant correlations were found between MBI-C score and [18 F]MK6240 retention or GM volume. CONCLUSION: We demonstrate for the first time a link between MBI and early AD pathology in a cognitively intact elderly population, supporting the use of the MBI-C as a metric to enhance clinical trial enrolment.


Assuntos
Amiloide/metabolismo , Biomarcadores , Voluntários Saudáveis/estatística & dados numéricos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Proteínas tau/metabolismo , Idoso , Encéfalo/metabolismo , Feminino , Humanos , Imagem por Ressonância Magnética , Masculino , Tomografia por Emissão de Pósitrons
19.
Alzheimers Dement ; 16(1): 49-59, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31784375

RESUMO

INTRODUCTION: The Advancing Research and Treatment in Frontotemporal Lobar Degeneration and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects longitudinal studies were designed to describe the natural history of familial-frontotemporal lobar degeneration due to autosomal dominant mutations. METHODS: We examined cognitive performance, behavioral ratings, and brain volumes from the first time point in 320 MAPT, GRN, and C9orf72 family members, including 102 non-mutation carriers, 103 asymptomatic carriers, 43 mildly/questionably symptomatic carriers, and 72 carriers with dementia. RESULTS: Asymptomatic carriers showed similar scores on all clinical measures compared with noncarriers but reduced frontal and temporal volumes. Those with mild/questionable impairment showed decreased verbal recall, fluency, and Trail Making Test performance and impaired mood and self-monitoring. Dementia was associated with impairment in all measures. All MAPT carriers with dementia showed temporal atrophy, but otherwise, there was no single cognitive test or brain region that was abnormal in all subjects. DISCUSSION: Imaging changes appear to precede clinical changes in familial-frontotemporal lobar degeneration, but specific early clinical and imaging changes vary across individuals.


Assuntos
Atrofia/patologia , Degeneração Lobar Frontotemporal , Predisposição Genética para Doença , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Testes Neuropsicológicos/estatística & dados numéricos , Proteína C9orf72/genética , Feminino , Degeneração Lobar Frontotemporal/genética , Degeneração Lobar Frontotemporal/patologia , Humanos , Estudos Longitudinais , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Progranulinas/genética , Lobo Temporal/patologia , Proteínas tau/genética
20.
Alzheimers Dement ; 16(1): 37-48, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31272932

RESUMO

INTRODUCTION: Some models of therapy for neurodegenerative diseases envision starting treatment before symptoms develop. Demonstrating that such treatments are effective requires accurate knowledge of when symptoms would have started without treatment. Familial frontotemporal lobar degeneration offers a unique opportunity to develop predictors of symptom onset. METHODS: We created dementia risk scores in 268 familial frontotemporal lobar degeneration family members by entering covariate-adjusted standardized estimates of brain atrophy into a logistic regression to classify asymptomatic versus demented participants. The score's predictive value was tested in a separate group who were followed up longitudinally (stable vs. converted to dementia) using Cox proportional regressions with dementia risk score as the predictor. RESULTS: Cross-validated logistic regression achieved good separation of asymptomatic versus demented (accuracy = 90%, SE = 0.06). Atrophy scores predicted conversion from asymptomatic or mildly/questionably symptomatic to dementia (HR = 1.51, 95% CI: [1.16,1.98]). DISCUSSION: Individualized quantification of baseline brain atrophy is a promising predictor of progression in asymptomatic familial frontotemporal lobar degeneration mutation carriers.


Assuntos
Atrofia/patologia , Demência Frontotemporal , Predisposição Genética para Doença , Mutação/genética , Testes Neuropsicológicos/estatística & dados numéricos , Encéfalo/patologia , Proteína C9orf72/genética , Feminino , Demência Frontotemporal/diagnóstico por imagem , Demência Frontotemporal/genética , Humanos , Processamento de Imagem Assistida por Computador/estatística & dados numéricos , Imagem por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Progranulinas/genética , Proteínas tau/genética
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