Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

TL;DR

This paper introduces a deep adversarial training method using cGANs with synthetic and real data to improve nuclei segmentation in histopathology images, addressing challenges like data scarcity and overlapping nuclei.

Contribution

It presents a novel adversarial framework that generates synthetic training data and enforces higher order consistency, outperforming traditional CNN methods in multi-organ nuclei segmentation.

Findings

Outperforms conventional CNN-based segmentation methods.

Generalizes well across different organs, sites, and disease states.

Effectively isolates overlapping nuclei.

Abstract

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.