AIMC Topic: Cell Shape

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Controlled surface morphology and hydrophilicity of polycaprolactone toward human retinal pigment epithelium cells.

Materials science & engineering. C, Materials for biological applications Nov 24, 2016
Applying scaffolds as a bed to enhance cell proliferation and even differentiation is one of the treatment of retina diseases such as age-related macular degeneration (AMD) which deteriorating photoreceptors and finally happening blindness. In this s...
Biocompatible Materials Water Cell Adhesion Porosity Hydrolysis Humans Retinal Pigment Epithelium Microscopy, Fluorescence Polyesters Hydrophobic and Hydrophilic Interactions Cell Shape Infant, Newborn Chloroform Immunohistochemistry Tissue Scaffolds Keratins Surface Properties Nanofibers
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Building cell models and simulations from microscope images.

Methods (San Diego, Calif.) Oct 17, 2015
The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source s...
Pattern Recognition, Automated Machine Learning Microscopy, Fluorescence Cell Shape Image Processing, Computer-Assisted Models, Biological Software Humans Computational Biology HeLa Cells Computer Simulation
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Optimizing parameters on alignment of PCL/PGA nanofibrous scaffold: An artificial neural networks approach.

International journal of biological macromolecules Oct 27, 2014
This paper proposes an artificial neural networks approach to finding the effects of electrospinning parameters on alignment of poly(ɛ-caprolactone)/poly(glycolic acid) blend nanofibers. Four electrospinning parameters, namely total polymer concentra...
Humans Neural Networks, Computer Reproducibility of Results Fibroblasts Cell Shape Polyglycolic Acid Polyesters Tissue Scaffolds Nanofibers Cell Adhesion Electricity
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Geometric deep learning and multiple-instance learning for 3D cell-shape profiling.

Cell systems Mar 19, 2025
The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understan...
Humans Imaging, Three-Dimensional Cell Shape Multiple-Instance Learning Algorithms Melanoma Drug Discovery Deep Learning
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Learning meaningful representation of single-neuron morphology via large-scale pre-training.

Bioinformatics (Oxford, England) Sep 1, 2024
SUMMARY: Single-neuron morphology, the study of the structure, form, and shape of a group of specialized cells in the nervous system, is of vital importance to define the type of neurons, assess changes in neuronal development and aging and determine...
Drosophila melanogaster Cell Shape Humans Rats Animals Deep Learning Neurons Datasets as Topic
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AI under the microscope: the algorithms powering the search for cells.

Nature Nov 1, 2023
Deep Learning Artificial Intelligence Microscopy Algorithms Cell Shape
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Classification of cell morphology with quantitative phase microscopy and machine learning.

Optics express Aug 3, 2020
We describe and compare two machine learning approaches for cell classification based on label-free quantitative phase imaging with transport of intensity equation methods. In one approach, we design a multilevel integrated machine learning classifie...
Humans Neural Networks, Computer Animals ROC Curve Image Processing, Computer-Assisted Machine Learning Algorithms Macrophages Cell Shape Area Under Curve Microscopy
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A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

Toxicological sciences : an official journal of the Society of Toxicology May 1, 2019
The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidim...
Sequestosome-1 Protein Cell Shape Primary Cell Culture Data Mining Heme Oxygenase-1 Cell Nucleus Gene Expression Regulation Kidney Tubules, Proximal Toxicology Risk Assessment Systems Biology Humans Cells, Cultured Kidney Diseases Machine Learning Databases, Factual
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Cell dynamic morphology analysis by deep convolutional features.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2017
Computational analysis of cell dynamic morphology in time-lapse image is a challenging task in biomedical research. Inspired by the recent success of deep learning, we investigate the possibility to apply a deep neural network to cell dynamic morphol...
Neural Networks, Computer Cell Shape
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