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1
Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens
Published 2021“…Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. …”
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2
Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens
Published 2021“…Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. …”
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3
Example of simulated calcium imaging dataset.
Published 2024“…In this study, we report an ROI selection method using a series of adaptive binarizations with a gaussian method and morphological image processing. The parameters for each operation such as the kernel size, sigma and footprint size were optimized. …”
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4
Probability density of each bin of accuracy.
Published 2024“…In this study, we report an ROI selection method using a series of adaptive binarizations with a gaussian method and morphological image processing. The parameters for each operation such as the kernel size, sigma and footprint size were optimized. …”
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6
Image4_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF
Published 2022“…The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. …”
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7
Image1_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF
Published 2022“…The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. …”
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8
Image3_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF
Published 2022“…The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. …”
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9
Image2_CNN-Based Cell Analysis: From Image to Quantitative Representation.TIF
Published 2022“…The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. …”
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10
DataSheet1_CNN-Based Cell Analysis: From Image to Quantitative Representation.pdf
Published 2022“…The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. …”
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11
Image processing workflow.
Published 2020“…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
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12
Flowchart scheme of the ML-based model.
Published 2024“…<b>I)</b> Testing data consisting of 20% of the entire dataset. <b>J)</b> Optimization of hyperparameter tuning. <b>K)</b> Algorithm selection from all models. …”
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13
PathOlOgics_RBCs Python Scripts.zip
Published 2023“…No segmented cell(s) occupied a space larger than 80x80 pixels, including the three-overlapping RBCs. As a result, the algorithm centred/padded each cell(s) within an 80x80 pixel-sized image, generating mask, cropped, and segmented images, all following a standardized naming convention that begins with the slide/smear number, followed by the patch number, and concludes with the (XYWH) coordinates. …”
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14
<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043)
Published 2025“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”