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Vol. 34, No. 8(3), S&M3042

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Vol. 32, No. 8(2), S&M2292

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Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials, Volume 35, Number 2(1) (2023)
Copyright(C) MYU K.K.
pp. 399-409
S&M3176 Research Paper of Special Issue
https://doi.org/10.18494/SAM4293
Published: February 9, 2023

Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence [PDF]

Yoichi Shimomoto, Kirin Inoue, Ikuo Yamamoto, Seigo Ohba, Kinuko Ogata, and Hideyuki Yamamoto

(Received December 30, 2022; Accepted February 1, 2023)

Keywords: cell nucleus detection, artificial intelligence, sliding window method, mask-RCNN method, oral cancer

We describe the detection of cell nuclei in oral cytology using artificial intelligence (AI). We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malignancy. As an initial step in the development of AI-assisted cytology, we investigated two methods for the automatic detection of cell nuclei in blue-stained cells in cytopreparation images. We evaluated the usefulness of the sliding window method (SWM) and the mask region-based convolutional neural network (Mask-RCNN) method in identifying cell nuclei in oral cytopreparation images. Thirty cases of liquid-based oral cytology were analyzed. First, we performed the SWM by dividing each image into 96 × 96 pixels. Overall, 591 images with or without blue-stained cell nuclei were prepared as the training data and 197 as the test data (from among 1576 images in total). Next, we performed the Mask-RCNN method by preparing 130 images of Class II and III lesions and creating mask images showing cell regions based on these images. By the SWM method, the highest detection rate for blue-stained cells in the evaluation group was found to be 0.9314. For Mask-RCNN, 37 cell nuclei were identified, and 1 cell nucleus was identified as a non-nucleus after 40 epochs (error rate: 0.027). Mask-RCNN was shown to be more accurate than SWM in identifying the cell nuclei. If the blue-stained cell nuclei can be correctly identified automatically, the entire cell morphology can be grasped faster, and the diagnostic performance of cytology can be improved.

Corresponding author: Yoichi Shimomoto


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This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Yoichi Shimomoto, Kirin Inoue, Ikuo Yamamoto, Seigo Ohba, Kinuko Ogata, and Hideyuki Yamamoto, Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence, Sens. Mater., Vol. 35, No. 2, 2023, p. 399-409.



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