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

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

Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence

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

Metal-containing peptide nucleic acid conjugates

Peptide Nucleic Acids (PNAs) are non-natural DNA/RNA analogues with favourable physico-chemical properties and promising applications. Discovered nearly 20 years ago, PNAs have recently re-gained quite a lot of attention. In this Perspective article, we discuss the latest advances on the preparation and utilisation of PNA monomers and oligomers containing metal complexes. These metal- conjugates have found applications in various research fields such as in the sequence-specific detection of nucleic acids, in the hydrolysis of nucleic acids and peptides, as radioactive probes or as modulators of PNA˙DNA hybrid stability, and last but not least as probes for molecular and cell biology