用于数字癌症组织学中多器官细胞核同时进行语义分割、实例分割和分类的三合一自底向上框架
A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology
AI总结：
提出一种三合一自底向上框架，通过带有独立加权损失的多解码器头生成语义分割、边缘提议和分类图，经多阶段后处理实现数字癌症组织学中多器官细胞核的同步实例分割与分类，性能显著提升且复杂度更低。
摘要：
数字组织学中细胞核的同时分割与分类在计算机辅助癌症诊断中起着至关重要的作用；然而，这仍然具有挑战性。目前实现的最高二值和多类全景质量（PQ）分别低至0.68 bPQ和0.49 mPQ。这是由于更高的染色变异性、组织间的变异性、粗糙的临床条件、重叠的细胞核以及细胞核类别不平衡所致。通用的深度学习方法通常依赖于端到端模型，这些模型无法解决明确与数字组织学相关的这些问题。在我们之前的工作DAN-NucNet中，我们使用端到端模型解决了语义分割的这些问题。这项工作扩展了我们之前的模型，以实现同时的实例分割和分类。我们引入了带有独立加权损失的额外解码器头，它们产生语义分割、边缘提议和分类图。我们使用三头模型的输出应用后处理，以产生最终的分割和分类。与大多数最先进方法所遵循的直接分割和分类策略相比，我们的多阶段方法利用了边缘提议和语义分割。由于这一点，我们证明了在产生高质量实例分割和细胞核分类方面的显著性能提升。我们在语义分割上取得了0.841的Dice分数，在实例分割上取得了0.713的bPQ分数，在细胞核分类上取得了0.633的mPQ。我们提出的框架在19种类型的组织上具有通用性。此外，与最先进的方法相比，该框架的复杂度更低。
英文摘要：
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.
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