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Blank Frame and Intensity Variation Distortion Detection and Restoration Pipeline for Phase-Contrast Microscopy Time-Lapse Images
(Aves, 2024) Ucar, Mahmut; Iheme, Leonardo O.; Onal, Sevgi; Pesen-Okvur, Devrim; Yalcin-Ozuysal, Ozden; Toreyin, Behcet U.; Unay, Devrim
In this study, we propose a preprocessing pipeline for the detection and correction of distorted frames in time-lapse images obtained from phase-contrast microscopy. The proposed pipeline employs the average intensities of frames as a foundational element for the analysis. In order to evaluate the degree of correction required for intensity variance, a normalization technique is applied to the difference between the average intensity of a specific frame and the median average intensity of all frames within the study. Our restoration method increases the histogram similarity between the distorted and non-distorted frames, preserves trans-passing pixels in regions of interest, and mitigates the development of additional distortions. The efficacy of the proposed method was evaluated using 15 395 time-lapse image frames from 27 experiments using our own dataset and 830 time-lapse images from four different experiments obtained from the cell tracking challenge. The results of the validation demonstrate a high degree of numerical and visual accuracy of the proposed pipeline.
Citation - WoS: 3
Citation - Scopus: 6
Improved 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, Devrim
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.
Citation - WoS: 5
Citation - Scopus: 5
Invadopodia: Proteolytic Feet of Cancer Cells
(TUBITAK, 2014) Batı, Gizem; Pesen Okvur, Devrim
The leading cause of death in cancer patients is metastasis. Invasion is an integral part of metastasis and is carried out by proteolytic structures called invadopodia at the cellular level. In this introductory review, we start by evaluating the definition of invadopodia. While presenting the upstream signaling events involved, we integrate current models on invadopodia. In addition, we discuss the significance of invadopodia in 2D and 3D and in vivo. We finally point out technical challenges and conclude with open questions in the field. © TÜBİTAK.

TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection

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