Methods toward in vivo measurement of zebrafish epithelial and deep cell proliferation.
We present a strategy for automatic classification and density estimation of epithelial enveloping layer (EVL) and deep layer (DEL) cells, throughout zebrafish early embryonic stages. Automatic cells classification provides the bases to measure the variability of relevant parameters, such as cells density, in different classes of cells and is finalized to the estimation of effectiveness and selectivity of anticancer drug in vivo. We aim at approaching these measurements through epithelial/deep cells classification, epithelial area and thickness measurement, and density estimation from scattered points. Our procedure is based on Minimal Surfaces, Otsu clustering, Delaunay Triangulation, and Within-R cloud of points density estimation approaches. In this paper, we investigated whether the distance between nuclei and epithelial surface is sufficient to discriminate epithelial cells from deep cells. Comparisons of different density estimators, experimental results, and extensively accuracy measurements are included.
Department of Electronics, Computer Sciences and Systems, Bologna University, Bologna, Italy. m.campana@unibo.it
Matteo
M
Campana
Benoit
B
Maury
Marie
M
Dutreix
Nadine
N
Peyriéras
Alessandro
A
Sarti
D000465:Algorithms; D000818:Animals; D002452:Cell Count / Q000706:statistics & numerical data; D049109:Cell Proliferation; D004847:Epithelial Cells / Q000145:classification / Q000166:cytology; D004848:Epithelium / Q000196:embryology; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D021621:Imaging, Three-Dimensional; D015027:Zebrafish / Q000196:embryology*