Abstract
The laser speckle contrast imaging allows the determination of the flow motion in a sequence of images. The aim of this study is to combine the speckle contrast imaging and machine learning methods to recognition of physiological fluids flow rate. Data on the flow of intralipid with average flow rate of 0-2 mm/s in a glass capillary were obtained using a developed experimental setup. These data were used to train a feed-forward artificial neural network. The accuracy of random image recognition was quite low due to pulsations and the uneven flow set by the pump. To increase the recognition accuracy, various methods for calculating speckle contrast were used. The best result was obtained when calculating the mean spatial speckle contrast. The application of the mean spatial speckle contrast imaging together with the proposed artificial neural network allowed to increase the fluid flow rate recognition accuracy from about 65 % to 89 % and make it possible to exclude an expert from the data processing.
Highlights
IntroductionAnalysis of changes in the properties of physiological fluids, in particular blood, can be used to diagnose various diseases
Analysis of changes in the properties of physiological fluids, in particular blood, can be used to diagnose various diseases. It is shown in the works [1,2,3] that changes in blood viscosity are associated with the regulation of immune responses
The laser Doppler flowmetry (LDF) and the laser speckle contrast imaging (LSCI) methods make it possible to carry out studies in the “in vivo” conditions to evaluate microcirculation in biological tissues [5,6,7]
Summary
Analysis of changes in the properties of physiological fluids, in particular blood, can be used to diagnose various diseases. It is shown in the works [1,2,3] that changes in blood viscosity are associated with the regulation of immune responses. The study of the rheological and kinematic properties of physiological fluids is an actual task, and of particular interest are minimally invasive and non-invasive diagnostic methods. As an alternative to manual segmentation of the obtained images, the authors propose the use of “sparse-UNET”. The authors propose the use of coronary CT angiography to reduce invasive procedures. The application of a convolutional neural network for segmentation and a support
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