Analysing biological images coming from the microscope is challenging; not only is it
complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using
automatic approaches that could learn and embrace that variance would be highly interesting for the
field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues.
Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a
segmentation software that uses a particle-based active contour method. This program needs the fine tuning of some hyperparameters that could present a long number of combinations, with the selection
of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically
selects the best possible parametrisation with which it can perform an accurate and non-supervised
segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential
of LimeSeg and optimise the parameters selection by adding automatisation. This methodology
has been applied to three datasets of confocal images from Drosophila melanogaster, where a good
convergence has been observed in the evaluation of the solutions. Our experimental results confirm
the proper performing of the algorithm, whose segmented images have been compared to those
manually obtained for the same tissues.Ministerio de Ciencia, Innovación y Universidades TIN2017-88209-C2Junta de Andalucía US-1263341Junta de Andalucía P18-RT-2778Ministerio de Economía, Industria y Competitividad BFU2016-74975-PMinisterio de Ciencia e Innovación PID2019-103900GB-10
