Automated detection of macrophages in quantitative phase images by deep learning using a mask region-based convolutional neural network

Kai Eder; Tobias Kutscher; Anne Marzi; Álvaro Barroso; Jürgen Schnekenburger; Björn Kemper

doi:10.1117/12.2577232

5 March 2021 Automated detection of macrophages in quantitative phase images by deep learning using a mask region-based convolutional neural network

Kai Eder, Tobias Kutscher, Anne Marzi, Álvaro Barroso, Jürgen Schnekenburger, Björn Kemper

Author Affiliations +

Proceedings Volume 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021; 116551K (2021) https://doi.org/10.1117/12.2577232
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

We explored a Mask Region-based Convolutional Neural Network (Mask R-CNN) to detect macrophages in quantitative phase images, which were acquired by digital holographic microscopy (DHM), an interferometry-based variant of quantitative phase imaging (QPI). The Mask R-CNN deep learning architecture is capable to detect and segment single macrophage cells in quantitative phase images and allows to perform both tasks in a multi-stage process. Our results show that the combined detection and segmentation of cells through Mask R-CNN-based automated evaluation prospects a fast and robust screening in label-free high throughput microscopy.

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Kai Eder, Tobias Kutscher, Anne Marzi, Álvaro Barroso, Jürgen Schnekenburger, and Björn Kemper "Automated detection of macrophages in quantitative phase images by deep learning using a mask region-based convolutional neural network", Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 116551K (5 March 2021); https://doi.org/10.1117/12.2577232

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