AIMC Topic: Microscopy

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Using Convolutional Neural Networks to Measure the Physiological Age of Caenorhabditis elegans.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Caenorhabditis elegans (C. elegans) is a popular and excellent model for studies of aging due to its short lifespan. Methods for precisely measuring the physiological age of C. elegans are critically needed, especially for antiaging drug screening an...
Animals Aging Image Processing, Computer-Assisted Neural Networks, Computer Microscopy Linear Models Computational Biology Caenorhabditis elegans Logistic Models
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Physics-based learning with channel attention for Fourier ptychographic microscopy.

Journal of biophotonics Nov 28, 2021
Fourier ptychographic microscopy (FPM) is a computational imaging technology for large field-of-view, high resolution and quantitative phase imaging. In FPM, low-resolution intensity images captured with angle-varying illumination are synthesized in ...
Fourier Analysis Neural Networks, Computer Attention Microscopy Lighting
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Reduced and stable feature sets selection with random forest for neurons segmentation in histological images of macaque brain.

Scientific reports Nov 26, 2021
In preclinical research, histology images are produced using powerful optical microscopes to digitize entire sections at cell scale. Quantification of stained tissue relies on machine learning driven segmentation. However, such methods require multip...
Image Processing, Computer-Assisted Neurons Machine Learning Macaca Brain Microscopy Algorithms Animals
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WBC-based segmentation and classification on microscopic images: a minor improvement.

F1000Research Nov 17, 2021
Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no...
Microscopy Humans Image Processing, Computer-Assisted Neural Networks, Computer Leukocytes
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High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Nov 7, 2021
Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational...
Unsupervised Machine Learning Microscopy Histological Techniques Models, Animal Mice Kidney Animals Lung Humans Brain
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Deep learning is widely applicable to phenotyping embryonic development and disease.

Development (Cambridge, England) Nov 5, 2021
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can auto...
Deep Learning Image Processing, Computer-Assisted Phenotype Animals Mutation Microscopy Embryonic Development Mice Disease Models, Animal Neural Networks, Computer Polycystic Kidney Diseases Xenopus Proteins Craniofacial Abnormalities Neurodevelopmental Disorders Xenopus laevis
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Evaluation of an Artificial Intelligence-Augmented Digital System for Histologic Classification of Colorectal Polyps.

JAMA network open Nov 1, 2021
IMPORTANCE: Colorectal polyps are common, and their histopathologic classification is used in the planning of follow-up surveillance. Substantial variation has been observed in pathologists' classification of colorectal polyps, and improved assessmen...
Humans Diagnostic Tests, Routine Image Interpretation, Computer-Assisted New Hampshire Microscopy Artificial Intelligence Data Accuracy Colorectal Neoplasms Colonic Polyps
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A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

Cell reports methods Oct 27, 2021
MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate...
Algorithms Deep Learning Neural Networks, Computer Image Processing, Computer-Assisted Microscopy
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Practical machine learning for disease diagnosis.

Cell reports methods Oct 25, 2021
Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process...
Microscopy Machine Learning Humans Neural Networks, Computer Deep Learning
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Deep learning for bone marrow cell detection and classification on whole-slide images.

Medical image analysis Oct 16, 2021
Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, ...
Deep Learning Bone Marrow Cells Microscopy
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