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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Segmentation of Multi-Dimensional Multi-Parametric Microscopic Data of Biological Samples Using Convolutional Neural Networks
 Backová, Lenka ; Benda, Aleš (advisor) ; Schätz, Martin (referee)
Multi-parametric highly dimensional images have become a standard way of imaging biological samples. To quantify results from these images, segmentation must be often applied first. However, due to the underlying shortcomings of the fluorescence microscopy of biological samples, i.e. low signal-to-noise ratio, convolutional neural networks have become widely used for automatization of the segmentation. Convolution neural networks showed to be versatile in their potential uses and able to segment complex images. In this work, we utilise neural network U-Net for segmentation of images, which contain not only intensity information, but fluorescence excited state lifetime information as well. We try different representations of the data to assess, whether the added information of the pixel values leads to improved performance. We present an application of the segmentation results with phasor analysis to study the fertility of mice sperm. 1
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