"The true mystery of the world is in visible, not in invisible"
-- Oscar Wilde
CELLPOSE (1,2, https://www.cellpose.org/) is a powerful image segmentation library , written in Python, based on AI and deep neuronal network approaches, developed specifically for microscopy and cell biology. It includes “model zoo” – a collection of several built-in models, obtained
Previous Next These pages will be dedicated to demonstration of profiles of different cortical areas, measured after segmentation of cortical sections. Segmentation was performed using the algorithm, described in the post “Nissl-stained sections segmentation using algorithms of Mathematical Morphology”.
The main part of presented algorithm is sequential homotopic thinning transformation, “invented” by a group of mathematicians, including F. Meyer (1976), Ch. Lantuejoul (1978), and headed by J.Serra, from Center for Mathematical Morphology, Fontainebleau, France. Many transformations based on thinning
Semantic segmentation of human brain cortex with UNET-like architecture using SONY Neural Network Console
Here we present better segmentation result using a modification of the UNET network published in version 1.5 of SONY Neural Network Console. Training images are prepared using the algorithm described here. Images were visually inspected and corrected, where necessary, using
Based on preliminary results of regression analysis, it seems possible to conclude that our initial hypothesis (here) was true: rich-club members areas differ from non-members by different topology of clusterization pattern. Cluster population regression data show that “rich-club” member candidates
High-resolution Nissl-stained sections of human brain were downloaded from Allen Brain Atlases portal using published API. Image resolution was 1 micrometer or better. Depending upon the section region, total size of JPEG files varied from 300MB to 1.5 GB. Between
Peer review: “Katrin Amunts, Vadim Istomin, Axel Schleicher, Karl Zilles. Postnatal development of the human primary motor cortex: a quantitative cytoarchitectonic analysis” Anat. Embryol., (1995) 192:557-571. and “K. Amunts, A. Schleicher, K.Zilles. Persistence of layer 4 in the primary motor
Why BigBrain data set is so important for cytoarchitectonic studies? The BigBrain model itself, among other goals (please, use this link for more information: BigBrain Project) was developed to be used as a publicly-available source of microscopical human brain data at