TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7149
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Article 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, DevrimIn 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.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.Article Citation - WoS: 9Citation - Scopus: 10Effects of Notch Signalling on the Expression of Sema3c, Hmga2, Cxcl14, Cxcr7, and Ccl20 in Breast Cancer(TÜBİTAK, 2019) Küçükköse, Cansu; Yalçın Özuysal, ÖzdenMetastasis is the main reason for death in breast cancer. Understanding the molecular players in metastasis is crucial for diagnostic and therapeutic purposes. Notch signalling plays an oncogenic role in breast tumorigenesis and is involved in metastasis. Downstream mediators of Notch signalling in prometastatic processes are not yet fully discovered. Here we aimed to investigate whether Notch signalling regulates the expression of SEMA3C, HMGA2, CXCL14, CXCR7, and CCL20, which are involved in prometastatic processes, in breast cell lines. To this end, expression of the selected genes was analysed following Notch activation by overexpression of the Notch1 intracellular domain in the normal breast epithelial cell line MCF10A, and inhibition by silencing of the Notch transcriptional mediator RBPj kappa in the breast cancer cell line MDA MB 231. SEMA3C and HMGA2 mRNA were decreased, while CXCL14 and CXCR7 mRNA were increased significantly in response to Notch activation in MCF10A cells. Notch inhibition in MDA MB 231 cells significantly decreased HMGA2 and CCL20 mRNA. Protein levels were not significantly altered by Notch modulation. In conclusion, we showed that Notch signalling regulates expression of SEMA3C, CXCL14, CCL20, CXCR7, and HMGA2, which are prominent candidate genes that might function downstream of Notch to induce prometastatic processes.