A new image segmentation method for quantitative analysis of in vitro scratch assay

Author(s):  
Aykut Erdamar ◽  
Sila Yilmaz ◽  
Tansel Uyar ◽  
Ozlem Darcansoy Iseri
Author(s):  
S.L. White ◽  
C.B. Jensen ◽  
D.D. Giera ◽  
D.A. Laska ◽  
M.N. Novilla ◽  
...  

In vitro exposure to LY237216 (9-Deoxo-11-deoxy-9,11-{imino[2-(2-methoxyethoxy)ethylidene]-oxy}-(9S)-erythromycin), a macrolide antibiotic, was found to induce cytoplasmic vacuolation in L6 skeletal muscle myoblast cultures (White, S.L., unpubl). The present study was done to determine, by autoradiographic quantitative analysis, the subcellular distribution of 3H-LY237216 in L6 cells.L6 cells (ATCC, CRL 1458) were cultured to confluency on polycarbonate membrane filters (Millipore Corp., Bedford, MA) in M-199 medium (GIBCO® Labs) with 10% fetal bovine serum. The cells were exposed from the apical surface for 1-hour to unlabelled-compound (0 μCi/ml) or 50 (μCi/ml of 3H-LY237216 at a compound concentration of 0.25 mg/ml. Following a rapid rinse in compound-free growth medium, the cells were slam-frozen against a liquid nitrogen cooled, polished copper block in a CF-100 cryofixation unit (LifeCell Corp., The Woodlands, TX). Specimens were dried in the MDD-C Molecular Distillation Drier (LifeCell Corp.), vapor osmicated and embedded in Spurrs low viscosity resin. Ultrathin sections collected on formvar coated stainless steel grids were counter-stained, then individually mounted on corks. A monolayer of Ilford L4 nuclear emulsion (Polysciences, Inc., Warrington, PA) was placed on the sections, utilizing a modified “loop method”. The emulsions were exposed for 7-weeks in a light-tight box at 4°C. Autoradiographs were developed in Microdol-X developer and examined on a Philips EM410LS transmission electron microscope. Quantitative analysis of compound localization employed the point and circle approach of Williams; incorporating the probability circle method of Salpeter and McHenry.


2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.


2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.


Bone Reports ◽  
2021 ◽  
Vol 14 ◽  
pp. 100865
Author(s):  
B.K. Davies ◽  
Andrew Hibbert ◽  
Mark Hopkinson ◽  
Gill Holdsworth ◽  
Isabel Orriss

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