Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis, Brazil.
Automation and Systems Department, Federal University of Santa Catarina, Florianópolis, Brazil.
Comput Med Imaging Graph. 2021 Jul;91:101934. doi: 10.1016/j.compmedimag.2021.101934. Epub 2021 May 15.
Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.
细胞学是一种低成本、非侵入性的诊断程序，用于支持广泛的病理学诊断。细胞通过抽吸或刮取从组织中采集，目前主要由经过广泛培训的医学或实验室专业人员手动进行。这是一个耗时且重复的过程，其中许多诊断标准是主观的，容易受到人为解释的影响。计算机视觉技术可以通过自动生成检查内容的定量和客观描述，帮助最大限度地减少误诊的机会并缩短分析所需的时间。为了确定目前应用于细胞学的计算机视觉技术的最新进展，我们进行了系统的文献综述，搜索了使用计算机视觉对细胞学载玻片上的细胞和细胞器进行分割、检测、量化和分类的方法。我们分析了过去 4 年发表的论文。初步搜索于 2020 年 9 月进行，结果得到 431 篇文章。在应用纳入/排除标准后，仍有 157 篇文章，我们对这些文章进行了分析，以了解该研究领域的趋势和存在的问题，突出了计算机视觉方法、染色技术、评估指标以及所使用数据集和计算机代码的可用性。结果表明，在分析的作品中，使用最多的方法是基于深度学习的（70 篇），而使用经典计算机视觉的作品较少（101 篇）。用于分类和目标检测的最常用指标是准确性（33 篇和 5 篇），而用于分割的指标是 Dice 相似性系数（38 篇）。关于染色技术，巴氏染色法应用最广泛（130 篇），其次是 H&E 染色法（20 篇）和 Feulgen 染色法（5 篇）。在论文中使用的 12 个数据集是公开可用的，其中 DTU/Herlev 数据集使用最广泛。我们得出的结论是，对于许多类型的染色，仍然缺乏高质量的数据集，而且大多数作品还不够成熟，无法在日常临床诊断常规中应用。我们还发现，采用基于深度学习的方法作为首选方法的趋势日益增长。
