Authors:
Shih-Ting Huang
;
Yue Jiang
and
Hao-Chiang Shao
Affiliation:
Department of Statistics and Information Science, Fu Jen Catholic University, Taiwan, Republic of China
Keyword(s):
Neuroblast, Soma Detection, Drosophila Brain, Confocal Microscopy.
Abstract:
To facilitate brain research, scientists need to identify factors that can promote or suppress neural cell differentiation mechanisms. Accordingly, the way to recognize, segment, and count developing neural cells within
a microscope image stack becomes a fundamental yet considerable issue. However, it is currently not feasible
to develop a DCNN (deep convolutional neural network) based segmentation algorithm for confocal fluorescence image stacks because of the lack of manual-annotated segmentation ground truth. Also, such tasks
traditionally require meticulous manual preprocessing steps, and such manual steps make the results unstable
even with software support like ImageJ. To solve this problem, we propose in this paper a convolution-based
algorithm for cell recognizing and counting. The proposed method is computationally efficient and nearly
parameter-free. For a 1024×1024×70 two-channel image volume containing about 100 developing neuron
cells, our method can finish the re
cognition and counting tasks within 250 seconds with a standard deviation
smaller than 4 comparing with manual cell-counting results
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