Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids

In oncology, two-dimensional in-vitro culture models are the standard test
beds for the discovery and development of cancer treatments, but in the
last decades, evidence emerged that such models have low predictive value
for clinical efficacy. Therefore they are increasingly complemented by
more physiologically relevant 3D models, such as spheroid micro-tumor
cultures. If suitable fluorescent labels are applied, confocal 3D image
stacks can characterize the structure of such volumetric cultures and, for
example, cell proliferation. However, several issues hamper accurate
analysis. In particular, signal attenuation within the tissue of the
spheroids prevents the acquisition of a complete image for spheroids over
100 micrometers in diameter. And quantitative analysis of large 3D image
data sets is challenging, creating a need for methods which can be applied
to large-scale experiments and account for impeding factors. We present a
robust, computationally inexpensive 2.5D method for the segmentation of
spheroid cultures and for counting proliferating cells within them. The
spheroids are assumed to be approximately ellipsoid in shape. They are
identified from information present in the Maximum Intensity Projection
(MIP) and the corresponding height view, also known as Z-buffer. It alerts
the user when potential bias-introducing factors cannot be compensated for
and includes a compensation for signal attenuation.