Current life science studies benefit from impressive imaging capabilities, but full realisation of their potential is often hindered by the difficulty of analysing high-content 2D/3D/4D images. Part of the problem is that automatic analyses, while very fast, often lack the discriminatory power of the human brain. In our labs we use 2D/3D/4D image analysis to unravel the cell behaviours that underlie organ development and regeneration. Given the limitations we have encountered with this approach, we have decided to develop a suite of napari tools to streamline the analysis that we and other groups do. This project requires extensive coding skills and familiarity with Python and Machine learning. There are two main areas of interest: quantify number and characterise the spatial location of multiple cell populations in 2D and 3D, and automatically segment, identify and measure bones from whole-body micro CT scans of mouse samples. The biological aspects will be supervised by Dr Alberto Rosello Diez, at the Australian Regenerative Medicine Institute, while the napari aspects will be supervised by Dr Juan Nunez-Iglesias, at the Dept. of Anatomy and Developmental Biology.
image analysis, development, multi-dimensional, machine learning, napari, limb growth, regeneration, microscopy
Australian Regenerative Medicine Institute (ARMI)
Biomedicine Discovery Institute (School of Biomedical Sciences) » Anatomy and Developmental Biology
Masters by research
15 Innovation Walk