Original title: Cell And Sub-Cellular Segmentation In Quantitative Phase Imaging Using U-Net
Authors: Majerčík, Jakub ; Špaček, Michal
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: The ability to automatically segment images, especially microscopy images of cells, opensnew opportunities in cancer research or other practical applications. Recent advancements in deeplearning enabled for effective single-cell segmentation, however, automatic segmentation of subcellularregions is still challenging. This work describes an implementation of a U-net neural networkfor label-free segmentation of sub-cellular regions on images of adherent prostate cancer cells,specifically PC-3 and 22Rv1. Using the best performing approach, out of all that have been tested,we have managed to distinguish between objects and background with average dice coefficients of0.83, 0.78 and 0.63 for whole cells, nuclei and nucleoli respectively
Keywords: cell segmentation; deep learning; neural network; nucleus,nucleolus; quantitative phase imaging
Host item entry: Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected papers, ISBN 978-80-214-5943-4

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200823

Permalink: http://www.nusl.cz/ntk/nusl-447867


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