AIMC Topic: Microscopy

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Rapid, automated nerve histomorphometry through open-source artificial intelligence.

Scientific reports
We aimed to develop and validate a deep learning model for automated segmentation and histomorphometry of myelinated peripheral nerve fibers from light microscopic images. A convolutional neural network integrated in the AxonDeepSeg framework was tra...

A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach.

Computers in biology and medicine
Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is difficult to diagnose, and accurate diagnosis is critical for effective treatment. Manual microscopic ana...

Detection and Recognition of Pollen Grains in Multilabel Microscopic Images.

Sensors (Basel, Switzerland)
Analysis of pollen material obtained from the Hirst-type apparatus, which is a tedious and labor-intensive process, is usually performed by hand under a microscope by specialists in palynology. This research evaluated the automatic analysis of pollen...

Deep Learning-Based Microbubble Localization for Ultrasound Localization Microscopy.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this ...

Semisolid Pharmaceutical Product Characterization Using Non-invasive X-ray Microscopy and AI-Based Image Analytics.

The AAPS journal
This work reports the use of X-ray microscopy (XRM) imaging to characterize the microstructure of semisolid formulations containing multiple immiscible phases. For emulsion-based semisolid formulations, the disperse phase globule size and its distrib...

High Resolution of Plasmonic Resonance Scattering Imaging with Deep Learning.

Analytical chemistry
The dark-field microscopy (DFM) imaging technology has the advantage of a high signal-to-noise ratio, and it is often used for real-time monitoring of plasmonic resonance scattering and biological imaging at the single-nanoparticle level. Due to the ...

A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning.

Journal of healthcare engineering
Vaginitis is a gynecological disease affecting the health of millions of women all over the world. The traditional diagnosis of vaginitis is based on manual microscopy, which is time-consuming and tedious. The deep learning method offers a fast and r...

Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning.

The Science of the total environment
Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this paper, microplastics from daily supplies in diverse chemical compositions and shapes are imaged by s...

Robotic and Microrobotic Tools for Dental Therapy.

Journal of healthcare engineering
Robotic and microrobotic tools such as dental operating microscopes and dental endoscopes are being used extensively in dental therapy, which have a significant impact on dental therapy and education. Herein, this paper reviews the state of the art o...

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.

Cells
The surgical pathology workflow currently adopted by clinics uses staining to reveal tissue architecture within thin sections. A trained pathologist then conducts a visual examination of these slices and, since the investigation is based on an empiri...