Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Book Part Citation - Scopus: 1Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application To Wound Healing and Cell Motility Assays of Breast Cancer(Elsevier, 2023) Erdem, Yusuf Sait; Ayanzadeh, Aydın; Mayalı, Berkay; Balıkçı, Muhammed; Belli, Özge Nur; Uçar, Mahmut; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Önal, Sevgi; Morani, Kenan; Iheme, Leonardo Obinna; Töreyin, Behçet UğurThis chapter describes a workflow for analyzing phase-contrast microscopy (PCM) data from two fundamental types of biomedical assays: assays for cell motility and assays for wound healing. The workflow of the analysis is composed of the methods for acquiring, restoring, segmenting, and quantifying biomedical data. In the literature, there have been separate methods aimed at specific stages of PCM data analysis. Nonetheless, there has never been a complete workflow for all stages of analysis. This work is an innovation that proposes an end-to-end workflow for image pre-processing, deep learning segmentation, tracking, and quantification stages in cell motility and wound healing assay analyses. The findings indicate that domain knowledge can be used to make simple but significant improvements to the results of cutting-edge methods. Furthermore, even for deep learning-based methods, pre-processing is clearly a necessary step in the workflow. © 2023 Elsevier Inc. All rights reserved.Article Citation - WoS: 3Citation - Scopus: 6Improved Cell Segmentation Using Deep Learning in Label-Free Optical Microscopy Images(TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2021) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, DevrimThe 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.Conference Object Citation - WoS: 3Citation - Scopus: 4Deep Learning Based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images(IEEE, 2020) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Okvur, Devrim Pesen; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, DevrimThe 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.Conference Object Citation - Scopus: 2Yara İyileşmesi Mikroskopi Görüntü Serilerinin Otomatik Analizi - Bir Ön-çalışma(IEEE, 2020) Mayalı, Berkay; Şaylığ, Orkun; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Ünay, DevrimCollective cell analysis from microscopy image series is important for wound healing research. Computer-based automation of such analyses may help in rapid acquisition of reliable and reproducible results. In this study phase -contrast optical microscopy image series of an in-vitro wound healing essay is manually delineated by two experts and its analysis is realized, traditional image processing and deep learning based approaches for automated segmentation of wound area are developed and their perlOrmance comparisons are carried out.Conference Object Citation - Scopus: 1A Preliminary Study on Cell Motility Analysis From Phase-Contrast Microscopy Image Series(IEEE, 2020) Kayan, Emre; Kavuşan, Tarık; Önal, Sevgi; Pesen Okvur, Devrim; Yalçın Özuysal, Özden; Töreyin, Behçet Uğur; Ünay, DevrimAnalyses of morphology, polarity, and motility of cells is important for cell biology research such as metastatic and invasive capacity of cells, wound healing, and embryonic development. Automation of such analyses using image series of phase-contrast optical microscopy, which allows label-free imaging of live cells in their living environment, is a need. With this purpose, in this study image series of a cell motility experiment is manually annotated, and an automation algorithm realizing motion and shape analyses of cells using the annotated data is developed. In addition, due to the low number of annotated data at hand, a U-Net based solution is devised for automated segmentation of the cells and its performance is evaluated.Conference Object Citation - WoS: 7Citation - Scopus: 11Cell Segmentation of 2d Phase-Contrast Microscopy Images With Deep Learning Method(Institute of Electrical and Electronics Engineers Inc., 2019) Ayanzadeh, Aydın; Yağar, Hüseyin Onur; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Unay, Devrim; Önal, SevgiThe quantitative and qualitative ascertainment of cell culture is integral to the robust determination of the cell structure analysis. Microscopy cell analysis and the epithet structures of cells in cell cultures are momentous in the fields of the biological research process. In this paper, we addressed the problem of phase-contrast microscopy under cell segmentation application. In our proposed method, we utilized the state-of-the-art deep learning models trained on our proposed dataset. Due to the low number of annotated images, we propose a multi-resolution network which is based on the U-Net architecture. Moreover, we applied multi-combination augmentation to our dataset which has increased the performance of segmentation accuracy significantly. Experimental results suggest that the proposed model provides superior performance in comparison to traditional state-of-the-art segmentation algorithms.Conference Object Citation - WoS: 1Citation - Scopus: 6Faz Kontrast Optik Mikroskopi Zaman Serisi Görüntülerinde Hücrelerin Otomatik Bölütlenmesi(Institute of Electrical and Electronics Engineers Inc., 2019) Binici, Rıfkı Can; Şahin, Umut; Ayanzadeh, Aydın; Töreyin, Behçet Uğur; Önal, Sevgi; Okvur, Devrim Pesen; Yalçın Özuysal, Özden; Ünay, DevrimFaz kontrast optik mikroskopi hücrelerin canlı ortamlarında zamana bağlı incelenmesi için tercih edilen görüntüleme yöntemidir. Bu yöntem ile elde edilen zaman serisi görüntülerinde hücrelerin bölütlenmesi işi hücre biyolojisi araştırmacılarının çözümüne ihtiyaç duyduğu emek yoğun ve zaman alan bir iştir. Bu çalışmada faz kontrast optik mikroskopi zaman serilerinde hücrelerin otomatik bölütlenmesi için geleneksel görüntü işleme ve derin öğrenme temelli yöntemler önerilmiş ve başarımları elle işaretlenmiş veri kümelerinde nicel olarak ölçülmüştür.