数字病理学中计算细胞核分割方法的全面综述
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
AI总结：
本文综述数字病理中从传统图像处理到深度学习的细胞核分割方法，重点讨论弱监督及不同模型优劣，并展望减少标注依赖、高效可解释的未来方向。
AI中文摘要：
在癌症诊断流程中，数字病理学在识别、分期和分级活检组织标本上的恶性区域方面发挥着关键作用。高分辨率组织学图像在外观上存在很大差异，其来源既包括采集设备，也包括 H&E 染色过程。细胞核分割是一项重要任务，因为它能从背景组织中检测出细胞核细胞，并产生细胞核的拓扑结构、大小和数量，而这些都是癌症检测的决定性因素。然而，对病理学家而言，这是一项相当耗时的任务，并且据报道具有很高的主观性。由现代人工智能（AI）模型赋能的计算机辅助诊断（CAD）工具使细胞核分割的自动化成为可能。这可以减少分析中的主观性并缩短阅片时间。本文提供了一项广泛综述，从使用传统图像处理技术的早期工作开始，一直延伸到遵循深度学习（DL）范式的现代方法。受标注数据稀缺这一事实推动，我们的综述还关注该问题的弱监督方面。最后，本文全面讨论了不同模型和监督类型的优势。此外，我们尝试推断并展望未来研究路线可能的发展方向，以便在保持高性能的同时最大限度地减少对标注数据的需求。未来的方法应强调高效且可解释的模型，其底层过程应透明，从而使医生能够信任其输出。
英文摘要：
In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.
