Short abstract We present a method for the fully automatic segmentation of cell nuclei from 3D confocal laser microscopy images based on the combination of wave propagation techniques and elastically deformable Fourier models. The resulting invariant shape descriptors will be used to build a statistical shape model for determining the average intersection lengths between cell nuclei and single ...

Abnormalities of the number and location of cells are hallmarks of both developmental and degenerative neurological diseases. However, standard stereological methods are impractical for assigning each cell's nucleus position within a large volume of brain tissue. We propose an automated approach for segmentation and localization of the brain cell nuclei in laser scanning microscopy (LSM) embryo...

We first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. Werefer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column.Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply asthe soft segmentation. The band-dependent posterior probability that the hard segmentation columnv...

Creating manual annotations in a large number of images is tedious bottleneck that limits deep learning use many applications. Here, we present study which used the output classical image analysis pipelineas labels when training convolutional neural network(CNN). This may not only reduce time experts spend annotating but it also lead to an improvement results compared from pipeline training. In...

Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns lineages. However, current datasets neuronal usually contain smaller than $10^{\text{-}3}\ mm^3$ with fewer 500 instances per volume, unable to reveal complexity in large brain regions restrict investigation structures. In this paper, we ha...