  Synthetic generation of three-dimensional cell models from histopathological
images aims to enhance understanding of cell mutation, and progression of
cancer, necessary for clinical assessment and optimal treatment. Classical
reconstruction algorithms based on image registration of consecutive slides of
stained tissues are prone to errors and often not suitable for the training of
three-dimensional segmentation algorithms. We propose a novel framework to
generate synthetic three-dimensional histological models based on a
generator-discriminator pattern optimizing constrained features that construct
a 3D model via a Blender interface exploiting smooth shape continuity typical
for biological specimens. To capture the spatial context of entire cell
clusters we deploy a novel deep topology transformer that implements and
attention mechanism on cell group images to extract features for the frozen
feature decoder. The proposed algorithms achieves high quantitative and
qualitative synthesis evident in comparative evaluation metrics such as a low
Frechet-Inception scores.
