Segmenting 3d biological data

I have recently been given the opportunity to study the segmentation of 3D data.  The group of Dr. C. Hammond of the School of Physiology, Pharmacology and Neuroscience in Bristol studies malformation in tissues of  Zebrafish  a model organism which can be relatively easily genetically manipulated.

A major task is to identify bone malformation or osteoarthritis. Hammond’s group manages to image hundreds of Zebrafish in there dimensions so that the bone structures can be visualised. Identifying bone deformations in the spine, for example, is key to associate them to specific genetic marker. To do so, a quantitative analysis of the structure of the individual vertebrae is necessary.

It turned out that it is possible to do this via image analysis techniques that are publicly available in Python: the key libraries that I employed are scipy. ndimage and scikit-image.  Identifying the vertebrae in 3d means to perform  a segmentation of volumes and surfaces in 3d images.

An example of the vertebrae, individually resolved, can be visualised in 3d here below:

 

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