Automated microscopy imaging systems facilitate high-throughput screening in molecular cellular biology research. The first step of these systems is cell nucleus segmentation, which has a great impact on the success of the overall system. The markercontrolled watershed is a technique commonly used by the previous studies for nucleus segmentation. These studies define their markers finding regional minima on the intensity/gradient and/or distance transform maps. They typically use the h-minima transform beforehand to suppress noise on these maps. The selection of the h value is critical; unnecessarily small values do not sufficiently suppress the noise, resulting in false and oversegmented markers, and unnecessarily large ones suppress too many pixels, causing missing and undersegmented markers. Because cell nuclei show different characteristics within an image, the same h value may not work to define correct markers for all the nuclei. To address this issue, in this work, we propose a new watershed algorithm that iteratively identifies its markers, considering a set of different h values. In each iteration, the proposed algorithm defines a set of candidates using a particular h value and selects the markers from those candidates provided that they fulfill the size requirement. Working with widefield fluorescence microscopy images, our experiments reveal that the use of multiple h values in our iterative algorithm leads to better segmentation results, compared to its counterparts. V C 2016 International Society for Advancement of Cytometry

Mesenchymal stem cells (MSCs) are promising candidates for cellular therapies due to their ability to migrate to damaged tissue without inducing immune reaction. Many techniques have been developed to trace MSCs and their differentiation efficacy; however, all of these methods have limitations. Conjugated polymer based water-dispersible nanoparticles (CPN) represent a new class of probes because they offer high brightness, improved photostability, high fluorescent quantum yield, and noncytotoxicity comparing to conventional dyes and quantum dots. We aimed to use this tool for tracing MSCs' fate in vitro and in vivo. MSC marker expression, survival, and differentiation capacity were assessed upon CPN treatment. Our results showed that after CPN labeling, MSC markers did not change and significant number of cells were found to be viable as revealed by MTT. Fluorescent signals were retained for 3 weeks after they were differentiated into osteocytes, adipocytes, and chondrocytes in vitro. We also showed that the labeled MSCs migrated to the site of injury and retained their labels in an in vivo liver regeneration model. The utilization of nanoparticle could be a promising tool for the tracking of MSCs in vivo and in vitro and therefore can be a useful tool to understand differentiation and homing mechanisms of MSCs.

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Özetçe -Bu makalede, Mezenkimal kök hücrelerinin (MKH) boyutunun ölçülmesi, hücrelerin sayımı ve takip edilmesi amacıyla çok çözünürlüklü süper piksel algoritması sunulmuştur. Çok çözünürlüklü süper pikseller, imgeye uygulanan degişken yogunluktaki süper piksel tohumları sayesinde elde edilmiştir. Bu tohumların yogunlukları, kanser kök hücre imgelerinde bulunan, bölgesel yüksek frekans bileşenlerine göre ayarlanmıştır. Böylece imgenin, çok çözünürlüklü süper piksellere ayrışmış hali elde edilmiştir. Bildirinin sundugu bir diger katkı da, benzer komşu süper piksel hücrelerini birleştiren yeni karar verme algoritmasıdır. Dalgacık teoreminin tek boyutlu sinyallerdeki kullanımşekline dayalı olarak geliştirilen bu algoritma, komşu süper piksellerin histogramlarına uygulanarak, benzerlikleri karşılaştırılmıştır. Sunulan bu algoritmanın kök hücre imgelerinde bulunan hücreleri başarılı birşekilde ayrıştırabildigi deneysel olarak da gösterilmiştir.Anahtar Kelimeler-Kök hücre takibi, çok çözünürlüklü süperpiksel, dalgacık dönüşümü, floresan imgesi.Abstract-A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multiresolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. An algorithm based on well known wavelet decomposition is developed and applied to the histograms of neighboring super pixels to exploit similarity. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images. 1