AFM image analysis of porous structures by means of neural networks. (January 2022)

• Record Type: Journal Article
• Authors: Giergiel, Magdalena; Zapotoczny, Bartlomiej; Czyzynska-Cichon, Izabela; Konior, Jerzy; Szymonski, Marek
• Is Part Of: Biomedical signal processing and control. Volume 71(2022)Part A
• Publication Date: 2022-01
• Subjects: Fenestrae -- Liver sinusoidal endothelial cells -- Atomic force microscopy -- Neural networks -- Machine learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103097
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A method using machine learning was developed to determine the porosity of liver endothelial cells' surface. AFM images were analysed using optimised neural network. Different distortions of AFM image were analysed. Abstract: In this work, we presented a method of finding and characterising transmembrane porous structures, called fenestrations, with the help of convolutional neural networks. Case studies are performed on high resolution AFM images of murine liver sinusoidal endothelial cells (LSECs). At first, we evaluated different kinds of noise occurring in the LSEC AFM measurements. Next, we proposed a schematic structure of the neural network suitable for our purpose. We examined different loss functions, optimising the accuracy of fenestration detection. Finally, we presented the method of rough calculation of fenestration size distributions. We demonstrated that the accuracy of this method surpasses 90%. Furthermore, it is fast, not sensitive to the chosen image contrast and fully deterministic. The simple scheme can be easily modified to different objects of interest, which promotes the use of neural networks as a universal tool for the analysis of various kinds of microscopy images.