  Though performed almost effortlessly by humans, segmenting 2D gray-scale or
color images into respective regions of interest (e.g.~background, objects, or
portions of objects) constitutes one of the greatest challenges in science and
technology as a consequence of several effects including dimensionality
reduction(3D to 2D), noise, reflections, shades, and occlusions, among many
other possibilities. While a large number of interesting related approaches
have been suggested along the last decades, it was mainly thanks to the recent
development of deep learning that more effective and general solutions have
been obtained, currently constituting the basic comparison reference for this
type of operation. Also developed recently, a multiset-based methodology has
been described that is capable of encouraging image segmentation performance
combining spatial accuracy, stability, and robustness while requiring little
computational resources (hardware and/or training and recognition time). The
interesting features of the multiset neurons methodology mostly follow from the
enhanced selectivity and sensitivity, as well as good robustness to data
perturbations and outliers, allowed by the coincidence similarity index on
which the multiset approach to supervised image segmentation is founded. After
describing the deep learning and multiset neurons approaches, the present work
develops comparison experiments between them which are primarily aimed at
illustrating their respective main interesting features when applied to the
adopted specific type of data and parameter configurations. While the deep
learning approach confirmed its potential for performing image segmentation,
the alternative multiset methodology allowed for enhanced accuracy while
requiring little computational resources.
