Cytology is a branch of pathology that deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. In the present work, the term cytology is used to indicate solid organ cytology. Automation in cytology started in the early 1950s with an aim to reduce manual efforts in the diagnosis of cancer. The influx of intelligent systems with high computational power and improved specimen collection techniques helped to achieve technological heights in the cytology automation process. In the present survey, we focus on image analysis techniques paving the way to automation in cytology. We take a short tour of 17 types of solid organ cytology to explore various segmentation and/or classification techniques that evolved during the past three decades to automate cytology image analysis. It is observed that most of the works are aligned toward three types of cytology: Cervical, Breast, and Respiratory tract cytology. These are discussed elaborately in the article. Commercial systems developed during the period are also summarized to comprehend the overall growth in respective domains. Finally, we discuss different state-of-the-art methods and related challenges to provide prolific and competent future research directions in bringing cytology-based commercial systems into the mainstream.

中文翻译：

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走向自动化的细胞学图像分析技术

细胞学是病理学的一个分支，它处理细胞的显微镜检查以诊断癌症或炎症状况。在目前的工作中，术语细胞学用于表示实体器官细胞学。细胞学自动化始于 1950 年代初期，旨在减少癌症诊断中的人工操作。具有高计算能力和改进的标本采集技术的智能系统的涌入有助于在细胞学自动化过程中达到技术高度。在本次调查中，我们专注于为细胞学自动化铺平道路的图像分析技术。我们简要介绍了 17 种实体器官细胞学，以探索在过去三年中发展起来的各种分割和/或分类技术，以实现细胞学图像分析的自动化。据观察，大多数工作都针对三种类型的细胞学：宫颈细胞学、乳腺细胞学和呼吸道细胞学。这些都在文章中详细讨论。还总结了在此期间开发的商业系统，以了解各个领域的整体增长情况。最后，我们讨论了不同的最先进的方法和相关挑战，以提供多产和有能力的未来研究方向，将基于细胞学的商业系统带入主流。