Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/11832
Title: Improved cell segmentation using deep learning in label-free optical microscopy images
Authors: Ayanzadeh, Aydın
Yalçın Özuysal, Özden
Pesen Okvur, Devrim
Önal, Sevgi
Töreyin, Behçet Uğur
Ünay, Devrim
Keywords: Segmentation
Breast cancer
Convolutional neural networks
Optical microscopy
Phase-contrast microscopy
Brightfield
Issue Date: 2021
Publisher: TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Abstract: The recently popular deep neural networks (DNNs) have a significant effect on the improvement of segmentation accuracy from various perspectives, including robustness and completeness in comparison to conventional methods. We determined that the naive U-Net has some lacks in specific perspectives and there is high potential for further enhancements on the model. Therefore, we employed some modifications in different folds of the U-Net to overcome this problem. Based on the probable opportunity for improvement, we develop a novel architecture by using an alternative feature extractor in the encoder of U-Net and replacing the plain blocks with residual blocks in the decoder. This alteration makes the model superconvergent yielding improved performance results on two challenging optical microscopy image series: a phase-contrast dataset of our own (MDA-MB-231) and a brightfield dataset from a well-known challenge (DSB2018). We utilized the U-Net with pretrained ResNet-18 as the encoder for the segmentation task. Hence, following the modifications, we redesign a novel skip-connection to reduce the semantic gap between the encoder and the decoder. The proposed skip-connection increases the accuracy of the model on both datasets. The proposed segmentation approach results in Jaccard Index values of 85.0% and 89.2% on the DSB2018 and MDA-MB-231 datasets, respectively. The results reveal that our method achieves competitive results compared to the state-of-the-art approaches and surpasses the performance of baseline approaches.
URI: https://doi.org/10.3906/elk-2105-244
https://hdl.handle.net/11147/11832
https://search.trdizin.gov.tr/yayin/detay/526977
ISSN: 1300-0632
1303-6203
Appears in Collections:Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
elk-29-si-1-18-2105-244.pdf3.19 MBAdobe PDFView/Open
Show full item record



CORE Recommender

SCOPUSTM   
Citations

3
checked on Feb 16, 2024

WEB OF SCIENCETM
Citations

1
checked on Feb 10, 2024

Page view(s)

41,966
checked on Feb 26, 2024

Download(s)

186
checked on Feb 26, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.