Cytological picture of the oral mucosa in patients with gastric and colon cancer

Kędra, Bożena; Chomczyk, M; Złotkowski, Marcin; Stokowska, W; Borsuk, A. D.; Bicz, Mieczysław; Pietruska, Małgorzata; Tokajuk, Grażyna; Charkiewicz, Radosław; Czajka, Piotr; Chyczewski, L; Zimnoch, L

doi:10.5603/19746

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Two methods are used to propose object region candidates: the sliding window method (SWM) (4)(5)(6)(7)(8)(9)(10) and the mask region-based convolutional neural network (Mask-RCNN) method. (11) The SWM extracts candidate regions by shifting the region to a fixed size at a fixed pixel interval. The extracted regions are applied to an image discriminator to determine the presence of important objects in the window.…”

Section: Introductionmentioning

confidence: 99%

Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence

Shimomoto¹,

Inoue²,

Yamamoto³

et al. 2023

Sensors and Materials

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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 bluestained cell nuclei can be correctly identified automatically, the entire cell morphology can be grasped faster, and the diagnostic performance of cytology can be improved.

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Section: Introductionmentioning

confidence: 99%

Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence

Shimomoto¹,

Inoue²,

Yamamoto³

et al. 2023

Sensors and Materials

View full text Add to dashboard Cite

show abstract

“…During image recognition using AI, the objects are detected and identified. Two methods are used for proposing the object region candidates: the sliding window method (SWM) [4][5][6][7][8][9][10] and the mask region-based convolutional neural network (Mask-RCNN) [11]. The SWM extracts candidate regions by shifting the region to a fixed size at a fixed pixel interval.…”

Section: Introductionmentioning

confidence: 99%

Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology

et al. 2022

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Background We aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. 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. Methods We evaluated the usefulness of the sliding window method (SWM) and mask region-based convolutional neural network (Mask-RCNN) in identifying the 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 (total: 1,576 images). Next, we performed the Mask-RCNN by preparing 130 images of Class II and III lesions and creating mask images showing cell regions based on these images. Results Using the SWM method, the highest detection rate for blue-stained cells in the evaluation group was 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). Conclusions Mask-RCNN is 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.

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Application of Bethesda terminology to categorize buccal epithelial smears among petroleum station workers in Taif city, KSA

Elesawy

hafez

Mohamed

et al. 2016

IJBAS

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Background and Objectives: Exposure to Petroleum products has a well-established mutagenicity and carcinogenicity of some components such as benzene. Buccal mucosal cells are capable of metabolizing the carcinogenic compounds to reactive chemicals. We investigated the buccal epithelial cell cytological changes resulting from the occupational exposure to Petroleum derivatives such as benzene. Methods: Samples were obtained from 70 Petrol station workers in Taif city-KSA, examined using the Papanicolaou stained smears and categorized based on Bethesda Terminology System. Statistical analysis was carried out with the SPSS. Results: About 38.6% of petrol station workers exhibited buccal cytological changes ranging from atypical squamous cells of undetermined significance (ASC-US) to high-grade squamous intraepithelial lesions (HSIL). Atypical squamous cells, cannot exclude highgrade squamous intraepithelial lesion (ASC-H) was the most frequent cytological abnormality. Buccal smears with abnormal cytological findings dominated among smokers in contrast to non-smokers with a statistically significant difference (P = 0.026). No significant associations were detected between the category of buccal cytological changes and the duration of work or the participant's age. Conclusion: Bethesda Terminology System can be efficiently applied to buccal smears. Petrol products induce precancerous epithelial changes in buccal epithelial cells indicating a potential health risk for Petrol station workers, thus annual checkup for those workers should be set as primary prevention of occupational-related cancer. Cigarette smoke is a co-factor that exacerbates the effects of Petrol derivatives.

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Cytological picture of the oral mucosa in patients with gastric and colon cancer

Cited by 3 publications

References 8 publications

Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence

Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence

Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology

Application of Bethesda terminology to categorize buccal epithelial smears among petroleum station workers in Taif city, KSA

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