Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images

TL;DR

This paper introduces a novel framework for generating synthetic 3D cancer cell models from histopathological images, improving the accuracy and utility of 3D reconstructions for cancer research and clinical applications.

Contribution

It presents a new generator-discriminator framework with a deep topology transformer for realistic 3D cell model synthesis from 2D images.

Findings

Achieves low Frechet-Inception scores indicating high-quality synthesis

Outperforms classical reconstruction algorithms in accuracy

Provides a biologically plausible 3D modeling approach

Abstract

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.