New development can analyze 4D images of cell division

The tool developed by the team is called CShaper.

It is a powerful computing tool that can

systematically segment and analyze cell images at the level of specific representatives. This is very important for studying cell division as well as cell and gene function.

Work text

In order to create a computationaldevice, biologists have studied how animals grow from a single cell, a fertilized egg, or through countless cell divisions. Specifically, they wanted to know the specific functions of genes, such as which genes are involved in cell division during the formation of various organs, or what causes abnormal cell divisions that cause tumors to grow.

For this, the researchers first obtainedimages of cells - their "family tree". They then “disable” (remove) the gene from the DNA sequence and compare the two pedigree trees to analyze the changes in the cells and infer the function of the genes. 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, which means it will take almost 20,000 experiments if you select one gene at a time. It will be a huge amount of data. Therefore, it is extremely important to use the automated image analysis system that we have developed, ”- the text of the study.

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.

Moreover, there is no reliable algorithm forsegmentation of time-lapse 3D images (ie 4D images) of cell division. Image segmentation is an important process in computer vision that involves dividing visual input into segments to facilitate image analysis. But researchers have to spend hundreds of hours manually marking many cell images.

The new development of CShaper can detect cell membranes, create cell shapes in 3D space, and more importantly, automatically segment cell images at the cell level.

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