Browsing by Subject "Image processing"

Now showing 1 - 7 of 7
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    Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms
    (Nature Research, 2021-10-21) Moebel, Emmanuel; Martínez Sánchez, Antonio; Lamm, Lorenz; Righetto, Ricardo; Wietrzynski, Wojciech; Albert, Sahradha; Lariviere, Damien; Fourmentin, Eric; Pfeffer, Stefan; Ortiz, Julio; Baumeister, Wolfgang; Peng, Tingying; Engel, Benjamin; Kervrann, Charles; Ingeniería de la Información y las Comunicaciones
    Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase–oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms.
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    Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
    (Elsevier, 2021-10-15) Sabzi, Sajad; Pourdarbani, Razieh; Rohban, Mohammad H.; García Mateos, Ginés; Arribas, J. I.; Informática y Sistemas; Facultades de la UMU::Facultad de Informática
    In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
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    Open Access
    GPU-based processing of Hartmann-Shack images for accurate and high-speed ocular wavefront sensing
    (Elsevier, 2019-02) Mompeán, J.; Aragón, J.L.; Prieto, P.; Artal, P.; Ingeniería y Tecnología de Computadores
    Hartmann–Shack aberrometry is a widely used technique in the field of visual optics but, high-speed and accurate processing of Hartmann–Shack images can be a computationally expensive/resource intensive task. While some advancements have been made in achieving high-performance processing units, they have not been specifically designed for processing Hartmann–Shack images of the human eye with Graphics Processing Units. In this work, we present the first full-Graphics Processing Unit implementation of a Hartmann–Shacksensor algorithm aimed at accurately measuring ocular aberrations at a high speed from high-resolution spot pattern images. The proposed algorithm, called PaPyCS (Parallel Pyramidal Centroid Search), is inherently parallel and performs a very robust centroid search to avoid image noise and other artifacts. This is a field where the use of Graphics Processing Units have not been exploited despite the fact that they can boost Adaptive Optics systems and related closed-loop approaches. Our proposed implementation achieves processing speeds of 380 frames per second for high resolution (1280x1280 pixels) images, in addition to showing a high resilience to system and image artifacts that appear in Hartmann–Shack images from human eyes: more than 98% of the Hartmann–Shack images, with aberrations of up to 4m Root Mean Square for a 5.12mm pupil diameter, were measured with less than 0.05m Root Mean Square Error, which is basically negligible for ocular aberrations.
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    Open Access
    Portable device for presbyopia correction with optoelectronic lenses driven by pupil response
    (Springer Nature, 2020-11-20) Mompeán, J.; Aragón, J.L.; Artal, P.; Aragón, J.L.; Artal, P.; Ingeniería y Tecnología de Computadores
    A novel portable device has been developed and built to dynamically, and automatically, correct presbyopia by means of a couple of opto-electronics lenses driven by pupil tracking. The system is completely portable providing with a high range of defocus correction up to 10 D. The glasses are controlled and powered by a smartphone. To achieve a truly real-time response, image processing algorithms have been implemented in OpenCL and ran on the GPU of the smartphone. To validate the system, different visual experiments were carried out in presbyopic subjects. Visual acuity was maintained nearly constant for a range of distances from 5 m to 20 cm.
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    Reliability of a New Semi-automatic Image Analysis Method for Evaluating the Doppler Signal and Intratendinous Vascular Resistance in Patellar Tendinopathy
    (2021-12) Martínez Payá, Jacinto Javier; Carrasco Martínez, Francisco; Ríos Díaz, José; Molina Payá, Francisco J.; Fisioterapia
    Abstract The aim of this study was to determine the intra- and inter-rater reliability of a new semi-automatic image analysis method for quantification of the shape of the Doppler signal and the intratendinous vascular resistance in patellar tendinopathy. Thirty athletes (27.4 y, standard deviation = 8.57 y) with patellar intratendinous vascularity were included in a cross-sectional study (42 tendons analyzed). The intratendinous blood flow was assessed with power Doppler and ImageJ (Version 1.50b, National Institutes of Health, Bethesda, MD, USA) quantification software over a manually selected region of interest. Two blinded observers performed the analysis of the Doppler signal (vascular resistance) and shape descriptors (number of signals, pixel intensity, area, perimeter, major diameter, minor diameter, circularity and solidity). The intraclass correlation coefficient (ICC) was calculated, and the Bland-Altman mean of differences (MoD) and 95% limits of agreement (LoA) were determined. Also, small real differences (SRDs) and the standard error of measurement (SEM) were calculated. Intra-rater reliability was at a maximum for area (ICC = 0.999, 95% confidence interval [CI] = 0.998-0.999) and at a minimum for solidity (ICC = 0.782, 95% CI: 0.682-0.853). The MoD and 95% LoA were very low, and the relative SRD and SEM were below 5.3% and 2%, respectively. The inter-rater reliability was the maximum for area (ICC = 0.993, 95% CI = 0.989-0.996) and the minimum for circularity (ICC = 0.73; 95% CI=0.611-0.817). The MoD and 95% LoA were low, with the SRD and SEM below 6% and 2.2%. The proposed quantitative method for studying the intratendinous Doppler signal in the patellar tendon is reliable and reproducible.
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    Open Access
    Robust membrane detection based on tensor voting for electron tomography
    (Elsevier, 2014-04) Martinez-Sanchez, Antonio; García, Inmaculada; Asano, Shoh; Lucic, Valdan; Fernandez, Jose-Jesus; Ingeniería de la Información y las Comunicaciones
    Electron tomography enables three-dimensional (3D) visualization and analysis of the subcellular architecture at a resolution of a few nanometers. Segmentation of structural components present in 3D images (tomograms) is often necessary for their interpretation. However, it is severely hampered by a number of factors that are inherent to electron tomography (e.g. noise, low contrast, distortion). Thus, there is a need for new and improved computational methods to facilitate this challenging task. In this work, we present a new method for membrane segmentation that is based on anisotropic propagation of the local structural information using the tensor voting algorithm. The local structure at each voxel is then refined according to the information received from other voxels. Because voxels belonging to the same membrane have coherent structural information, the underlying global structure is strengthened. In this way, local information is easily integrated at a global scale to yield segmented structures. This method performs well under low signal-to-noise ratio typically found in tomograms of vitrified samples under cryo-tomography conditions and can bridge gaps present on membranes. The performance of the method is demonstrated by applications to tomograms of different biological samples and by quantitative comparison with standard template matching procedure.
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    Simulating the cellular context in synthetic datasets for cryo-electron tomography
    (Institute of Electrical and Electronics Engineers, 2024-05-08) Martinez-Sanchez, Antonio; Lamm, Lorenz; Jasnin, Marion; Phelippeau, Harold; Ingeniería de la Información y las Comunicaciones
    Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms. Current simulators in cryo-ET focus on reproducing distortions from image acquisition and tomogram reconstruction, however, they can not generate many of the low order features present in cellular tomograms. Here we propose several geometric and organization models to simulate low order cellular structures imaged by cryo-ET. Specifically, clusters of any known cytosolic or membrane bound macromolecules, membranes with different geometries as well as different filamentous structures such as microtubules or actin-like networks. Moreover, we use parametrizable stochastic models to generate a high diversity of geometries and organizations to simulate representative and generalized datasets, including very crowded environments like those observed in native cells. These models have been implemented in a multiplatform open-source Python package, including scripts to generate cryo-tomograms with adjustable sizes and resolutions. In addition, these scripts provide also distortion-free density maps besides the ground truth in different file formats for efficient access and advanced visualization. We show that such a realistic synthetic dataset can be readily used to train generalizable deep learning algorithms.