Simultaneous segmentation and classification of nuclei in digital histology play an essential role in computer-assisted cancer diagnosis; however, it remains challenging. The highest achieved binary and multi-class Panoptic Quality (PQ) remains as low as 0.68 bPQ and 0.49 mPQ, respectively. It is due to the higher staining variability, variability across the tissue, rough clinical conditions, overlapping nuclei, and nuclear class imbalance. The generic deep-learning methods usually rely on end-to-end models, which fail to address these problems associated explicitly with digital histology. In our previous work, DAN-NucNet, we resolved these issues for semantic segmentation with an end-to-end model. This work extends our previous model to simultaneous instance segmentation and classification. We introduce additional decoder heads with independent weighted losses, which produce semantic segmentation, edge proposals, and classification maps. We use the outputs from the three-head model to apply post-processing to produce the final segmentation and classification. Our multi-stage approach utilizes edge proposals and semantic segmentations compared to direct segmentation and classification strategies followed by most state-of-the-art methods. Due to this, we demonstrate a significant performance improvement in producing high-quality instance segmentation and nuclei classification. We have achieved a 0.841 Dice score for semantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633 mPQ for nuclei classification. Our proposed framework is generalized across 19 types of tissues. Furthermore, the framework is less complex compared to the state-of-the-art.

翻译：在数字组织学中，细胞核的同步分割与分类在计算机辅助癌症诊断中起着关键作用，但仍具挑战性。目前最高水平的二分类和多分类全景质量得分分别仅为0.68 bPQ和0.49 mPQ。这是由于染色变异性大、组织间差异显著、临床条件粗糙、细胞核重叠以及核类别不平衡等问题所致。通用的深度学习方法通常依赖端到端模型，难以解决这些与数字组织学密切相关的特定问题。在我们先前的工作DAN-NucNet中，我们通过端到端模型解决了语义分割中的这些问题。本研究将先前模型扩展到同步实例分割与分类。我们引入了带有独立加权损失的额外解码器头部，用于生成语义分割、边缘提议和分类映射。我们利用三头模型的输出进行后处理，以产生最终的分割与分类结果。与大多数现有方法采用的直接分割与分类策略不同，我们的多阶段方法利用了边缘提议和语义分割。由此，我们在生成高质量实例分割与细胞核分类方面展示了显著的性能提升。我们实现了语义分割的0.841 Dice分数、实例分割的0.713 bPQ分数以及细胞核分类的0.633 mPQ分数。所提出的框架可泛化至19种组织类型。此外，该框架相比现有方法复杂度更低。