The tool developed by the team is called CShaper.
It is a powerful computing tool that can
Systematically segment and analyze images of cells at the level of specific representatives.This is very important for the study of cell division as well as the functions of cells and genes.Work text
In order to create a computing device, biologists studied how animals grow upfrom a single cell, a fertilized egg, or through countless cell divisions.In particular, they wanted to know certain functions of genes, such as which genes are involvedin the division of cells in the formation of different organs or that causesabnormal cell divisions that cause tumors to grow.
To do this, the researchers first obtained images of the cells — their "family tree."They then "turn off" (delete) the gene from the DNA sequence and compareTwo family trees to analyze changes in cells and infer gene functions.After that, the experiment is repeated, but with different genes.
Biologists used embryos in the studyCaenorhabditis elegans is a type of worm that shares many biological characteristics with humans, so it can be used to study tumor growth in humans.
"Given that C. elegans has about 20,000 genes, that means it would take almost 20,000 experiments if you pick one gene at a time.Therefore, it is extremely important to use the automated image analysis system that we have developed." investigations.
Cell images are usually obtained usingscanning with a laser beam. Existing image analysis systems can only successfully detect the cell nucleus, but the image of the cell membrane will be of poor quality, which makes it difficult to reconstruct the cell shape.
In addition, there is no reliable algorithm for segmenting frame-by-frame three-dimensional images (i.e., four-dimensional images) of cell division.Image segmentation is an important process in computer vision that includesDivide visual input into segments to simplify image analysis.But researchers have to spend hundreds of hours manually labeling many images of cells.
CShaper's new development can detect cell membranes, create cell shapes in three-dimensional space, and more importantly, automatically segment cell images at the cell level.
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