  Nuclei segmentation is a fundamental task that is critical for various
computational pathology applications including nuclei morphology analysis, cell
type classification, and cancer grading. Conventional vision-based methods for
nuclei segmentation struggle in challenging cases and deep learning approaches
have proven to be more robust and generalizable. However, CNNs require large
amounts of labeled histopathology data. Moreover, conventional CNN-based
approaches lack structured prediction capabilities which are required to
distinguish overlapping and clumped nuclei. Here, we present an approach to
nuclei segmentation that overcomes these challenges by utilizing a conditional
generative adversarial network (cGAN) trained with synthetic and real data. We
generate a large dataset of H&E training images with perfect nuclei
segmentation labels using an unpaired GAN framework. This synthetic data along
with real histopathology data from six different organs are used to train a
conditional GAN with spectral normalization and gradient penalty for nuclei
segmentation. This adversarial regression framework enforces higher order
consistency when compared to conventional CNN models. We demonstrate that this
nuclei segmentation approach generalizes across different organs, sites,
patients and disease states, and outperforms conventional approaches,
especially in isolating individual and overlapping nuclei.
