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Title: Deep learning based segmentation pipeline for label-free phase-contrast microscopy images
Authors: Ayanzadeh, Aydın
Yalçın Özuysal, Özden
Okvur, Devrim Pesen
Önal, Sevgi
Töreyin, Behçet Uğur
Ünay, Devrim
Keywords: Cell segmentation
Phase-Contrast microscopy
Deep learning
Issue Date: 2020
Publisher: IEEE
Abstract: The segmentation of cells is necessary for biologists in the morphological statistics for quantitative and qualitative analysis in Phase-contrast Microscopy (PCM) images. In this paper, we address the cell segmentation problem in PCM images. Deep Neural Networks (DNNs) commonly is initialized with weights from a network pre-trained on a large annotated data set like ImageNet have superior performance than those trained from scratch on a small dataset. Here, we demonstrate how encoder-decoder type architectures such as U-Net and Feature Pyramid Network (FPN) can be improved by an alternative encoder which pre-trained on the ImageNet dataset. In particular, our experimental results confirm that the image descriptors from ResNet-18 are highly effective in accurate prediction of the cell boundary and have higher Intersection over Union (IoU) in comparison to the classical U-Net and require fewer training epochs.
Description: 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK -- Istanbul Medipol Univ
ISBN: 978-1-7281-7206-4
ISSN: 2165-0608
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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