Abstract
Synthetic generation of three-dimensional cell models from histopathological images enables an enhanced understanding of cell mutation, spatial context, 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. Simultaneously a discriminator is trained to distinguish between the original cell patches and projections of the three-dimensional model. To capture the spatial context of entire cell clusters we deploy a similar architecture expanded by style transfer capability. Models based on clusters of adjoined embedding points are generated to analyze the characteristic cell mutation process of breast cancer.
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URL
https://arxiv.org/abs/2101.11600
