  Multiplex tissue immunostaining is a technology of growing relevance as it
can capture in situ the complex interactions existing between the elements of
the tumor microenvironment. The existence and availability of large, annotated
image datasets is key for the objective development and benchmarking of
bioimage analysis algorithms. Manual annotation of multiplex images, is
however, laborious, often impracticable. In this paper, we present Synplex, a
simulation system able to generate multiplex immunostained in situ tissue
images based on user-defined parameters. This includes the specification of
structural attributes, such as the number of cell phenotypes, the number and
level of expression of cellular markers, or the cell morphology. Synplex
consists of three sequential modules, each being responsible for a separate
task: modeling of cellular neighborhoods, modeling of cell phenotypes, and
synthesis of realistic cell/tissue textures. Synplex flexibility and accuracy
are demonstrated qualitatively and quantitatively by generating synthetic
tissues that simulate disease paradigms found in the real scenarios. Synplex is
publicly available for scientific purposes, and we believe it will become a
valuable tool for the training and/or validation of multiplex image analysis
algorithms.
