  We propose an automatic 3D segmentation algorithm for multiphoton microscopy
images of microglia. Our method is capable of segmenting tubular and blob-like
structures from noisy images. Current segmentation techniques and software fail
to capture the fine processes and soma of the microglia cells, useful for the
study of the microglia role in the brain during healthy and diseased states.
Our coupled tubularity flow field (TuFF)-blob flow field (BFF) method evolves a
level set toward the object boundary using the directional tubularity and
blobness measure of 3D images. Our method found a 20% performance increase
against state of the art segmentation methods on a dataset of 3D images of
microglia even in images with intensity heterogeneity throughout the object.
The coupled TuFF-BFF segmentation results also yielded 40% improvement in
accuracy for the ramification index of the processes, which displays the
efficacy of our method.
