Application of deep convolutional and long short-term memory neural networks to red blood cells motion detection and velocity approximation

Kornaev, Alexey; Dremin, Viktor; Kornaeva, Elena; Volkov, Mikhail V.

doi:10.1117/12.2626040

Cited by 1 publication

(1 citation statement)

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“…Paper [17] aims to compute the velocity of red blood cells by employing two methods. First method employs analysis of intensity of pixels for detecting object and for computing velocity of red blood cells and displacement in a capillary.…”

Section: Object Detectionmentioning

confidence: 99%

Deep learning based object detection in nailfold capillary images

Venkatapathiah

Selvan

Nanda

et al. 2023

IJ-AI

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Microcirculation in a subject can be examined and pathological changes can be assessed by utilizing capillaroscopy, which is a very safe, convenient and non-invasive approach. Using a microscope, doctors view the capillaries by looking through nailfold epidermis. Nailfold anatomy is ideal to evaluate the microcirculation and detect various diseases caused by vascular damages. Rheumatologists evaluate systemic diseases which involve damage in vasculature, by analyzing the red blood cells within the capillaries. Sometimes, capillary morphology may be useful as an early indicator while, severity of damage in capillary architecture may indicate internal organ involvement. Thus, in a capillaroscopic assessment, the doctor examines modifications in morphological and functional aspects of capillaries. These comprise of capillary diameter, visibility, distribution, length, microhemorrhages, blood flow and density. In this paper, a novel object detection algorithm is proposed based on deep learning architectures for detecting and locating various capillary loops in the nailfold region. Various characteristic features are extracted from the capillaries through image processing algorithms and in turn an attempt is made to differentiate between images of diseased subjects and healthy controls.

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Section: Object Detectionmentioning

confidence: 99%