Browsing by Subject "Cell lines"

Now showing 1 - 5 of 5

Open Access
Canlı hücre bölütlemesi için gözeticili öğrenme modeli
(IEEE Computer Society, 2014-04) Koyuncu, Can Fahrettin; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem
Automated cell imaging systems have been proposed for faster and more reliable analysis of biological events at the cellular level. The first step of these systems is usually cell segmentation whose success affects the other system steps. Thus, it is critical to implement robust and efficient segmentation algorithms for the design of successful systems. In the literature, the most commonly used methods for cell segmentation are marker controlled watersheds. These watershed algorithms assume that markers one-to-one correspond to cells and identify their boundaries by growing these markers. Thus, it is very important to correctly define the markers for these algorithms. The markers are usually defined by finding local minima/maxima on intensity or gradient values or by applying morphological operations on the corresponding binary image. In this work, we propose a new marker controlled watershed algorithm for live cell segmentation. The main contributions of this algorithm are twofold. First, different than the approaches in the literature, it implements a new supervised learning model for marker detection. In this model, it has been proposed to extract features for each pixel considering its neighbors' intensities and gradients and to decide whether this pixel is a marker pixel or not by a classifier using these extracted features. Second, it has been proposed to group the neighboring pixels based on the direction information and to extract features according to these groups. The experiments on 1954 cells show that the proposed algorithm leads to higher segmentation results compared to other watersheds. © 2014 IEEE.
Open Access
Carcinoma cell line discrimination in microscopic images using unbalanced wavelets
(IEEE, 2012-03) Keskin, Furkan; Suhre, Alexander; Erşahin, Tüli,; Çetin Atalay, Rengül; Çetin, A. Enis
Cancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE.
Open Access
Evaluation of an aldo-keto reductase gene signature with prognostic significance in colon cancer via activation of epithelial to mesenchymal transition and the p70S6K pathway
(Oxford University Press, 2020-07) Demirkol Canlı, S.; Seza, E. G.; Sheraj, I.; Gömçeli, İ.; Turhan, N.; Carberry, S.; Prehn, J. H. M.; Güre, Ali Osmay; Banerjee, S.
AKR1B1 and AKR1B10, members of the aldo-keto reductase family of enzymes that participate in the polyol pathway of aldehyde metabolism, are aberrantly expressed in colon cancer. We previously showed that high expression of AKR1B1 (AKR1B1HIGH) was associated with enhanced motility, inflammation and poor clinical outcome in colon cancer patients. Using publicly available datasets and ex vivo gene expression analysis (n = 51, Ankara cohort), we have validated our previous in silico finding that AKR1B1HIGH was associated with worse overall survival (OS) compared with patients with low expression of AKR1B1 (AKR1B1LOW) samples. A combined signature of AKR1B1HIGH and AKR1B10LOW was significantly associated with worse recurrence-free survival (RFS) in microsatellite stable (MSS) patients and in patients with distal colon tumors as well as a higher mesenchymal signature when compared with AKR1B1LOW/AKR1B10HIGH tumors. When the patients were stratified according to consensus molecular subtypes (CMS), AKR1B1HIGH/AKR1B10LOW samples were primarily classified as CMS4 with predominantly mesenchymal characteristics while AKR1B1LOW/AKR1B10HIGH samples were primarily classified as CMS3 which is associated with metabolic deregulation. Reverse Phase Protein Array carried out using protein samples from the Ankara cohort indicated that AKR1B1HIGH/AKR1B10LOW tumors showed aberrant activation of metabolic pathways. Western blot analysis of AKR1B1HIGH/AKR1B10LOW colon cancer cell lines also suggested aberrant activation of nutrient-sensing pathways. Collectively, our data suggest that the AKR1B1HIGH/AKR1B10LOW signature may be predictive of poor prognosis, aberrant activation of metabolic pathways, and can be considered as a novel biomarker for colon cancer prognostication.
Open Access
Gauss tabanlı modelleme kullanarak canlı hücre görüntülerinin öğreticisiz bölütlenmesi
(2011-04) Arslan, Salim; Durmaz, İrem; Çetin-Atalay, Rengül; Gündüz-Demir, Çiğdem
The first step of targeted cancer drug development is to screen and determine drug candidates by in vitro measuring the effectiveness of the drugs. The tests developed for this purpose can be time consuming due to their procedures and cannot be conducted in every laboratory due to the required hardwares. On the other hand, an image-based screening test has a potential to be less time consuming since it can directly be carried out on the live cell images and to be more extensively used because of the availability of its required equipments and their relatively less expensive cost. With such an image-based test, it is possible to quantify the cell death by finding cellular regions and comparing it against the control group. In this work, we propose a new method that automatically locates the cellular regions by the unsupervised segmentation of live cell images. This method relies on approximately locating cellular regions and the background with gradient-based thresholding and morphological operators and then finding the final boundaries by modeling the gradient of these regions with Gaussians. Working on the images of different cell lines captured with different magnifications, our experiments show that the proposed method leads to promising results. © 2011 IEEE.
Open Access
Smart markers for watershed-based cell segmentation
(2012) Koyuncu, Can Fahrettin
Automated cell imaging systems facilitate fast and reliable analysis of biological events at the cellular level. In these systems, the first step is usually cell segmentation that greatly affects the success of the subsequent system steps. On the other hand, similar to other image segmentation problems, cell segmentation is an ill-posed problem that typically necessitates the use of domain specific knowledge to obtain successful segmentations even by human subjects. The approaches that can incorporate this knowledge into their segmentation algorithms have a potential to greatly improve the segmentation results. In this study, we propose a new approach for the effective segmentation of live cells from phase-contrast microscopy. This approach introduces a new set of “smart markers” for a marker-controlled watershed algorithm, for which the identification of its markers is critical. The proposed approach relies on using domain specific knowledge, in the form of visual characteristics of the cells, to define the markers. We evaluate our approach on a total of 1954 cells. The experimental results demonstrate that the proposed approach is quite effective in identifying better markers compared to its counterparts. This will in turn be effective in improving the segmentation performance of a marker-controlled watershed algorithm.