In the last years, it has become increasingly clear that neurodegenerative diseases involve protein aggregation, a
process often used as disease progression readout and to develop therapeutic strategies. This work presents an image
processing tool to automatic segment, classify and quantify these aggregates and the whole 3D body of the nematode
A total of 150 data set images, containing different slices, were captured with a confocal microscope from animals
of distinct genetic conditions. Because of the animals' transparency, most of the slices pixels appeared dark, hampering
their body volume direct reconstruction. Therefore, for each data set, all slices were stacked in one single 2D image in
order to determine a volume approximation. The gradient of this image was input to an anisotropic diffusion algorithm
that uses the Tukey's biweight as edge-stopping function. The image histogram median of this outcome was used to
dynamically determine a thresholding level, which allows the determination of a smoothed exterior contour of the worm
and the medial axis of the worm body from thinning its skeleton. Based on this exterior contour diameter and the medial
animal axis, random 3D points were then calculated to produce a volume mesh approximation. The protein aggregations
were subsequently segmented based on an iso-value and blended with the resulting volume mesh.
The results obtained were consistent with qualitative observations in literature, allowing non-biased, reliable and
high throughput protein aggregates quantification. This may lead to a significant improvement on neurodegenerative
diseases treatment planning and interventions prevention.