  Cytology is a low-cost and non-invasive diagnostic procedure employed to
support the diagnosis of a broad range of pathologies. 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. We analyzed papers published in the
last 5 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.
