AIMC Topic: Erythrocytes

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An automated malaria cells detection from thin blood smear images using deep learning.

Tropical biomedicine
Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effect...

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network.

Archives of pathology & laboratory medicine
CONTEXT.—: The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges.

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

Optics express
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morpholog...

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Optics letters
Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are...

Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling m...

High-throughput label-free cell detection and counting from diffraction patterns with deep fully convolutional neural networks.

Journal of biomedical optics
SIGNIFICANCE: Digital holographic microscopy (DHM) is a promising technique for the study of semitransparent biological specimen such as red blood cells (RBCs). It is important and meaningful to detect and count biological cells at the single cell le...

Applications of deep learning to the assessment of red blood cell deformability.

Biorheology
BACKGROUND: Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by...

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Optics express
Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence propert...

Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.

Journal of biomedical optics
SIGNIFICANCE: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase im...

Red blood cell classification in lensless single random phase encoding using convolutional neural networks.

Optics express
Rapid cell identification is achieved in a compact and field-portable system employing single random phase encoding to record opto-biological signatures of living biological cells of interest. The lensless, 3D-printed system uses a diffuser to encode...