{"created":"2023-05-15T16:49:53.341735+00:00","id":27529,"links":{},"metadata":{"_buckets":{"deposit":"ebce44b6-a62b-409d-9192-95d196005238"},"_deposit":{"created_by":6,"id":"27529","owners":[6],"pid":{"revision_id":0,"type":"depid","value":"27529"},"status":"published"},"_oai":{"id":"oai:nagasaki-u.repo.nii.ac.jp:00027529","sets":["10:11"]},"author_link":["122357","122359","122358","122364","122363","122356","122355","122362","122360","122361"],"item_2_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2022-08-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicPageStart":"62","bibliographicVolumeNumber":"17","bibliographic_titles":[{"bibliographic_title":"Diagnostic Pathology"}]}]},"item_2_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"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.\nMethods: 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.\nResults: 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).\nConclusions: 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.","subitem_description_type":"Abstract"}]},"item_2_description_63":{"attribute_name":"引用","attribute_value_mlt":[{"subitem_description":"Diagnostic Pathology, 17 (1), art. no. 62; 2022","subitem_description_type":"Other"}]},"item_2_publisher_33":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"BMC"}]},"item_2_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1186/s13000-022-01245-0","subitem_relation_type_select":"DOI"}}]},"item_2_rights_13":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data."}]},"item_2_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1746-1596","subitem_source_identifier_type":"ISSN"}]},"item_2_version_type_16":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Mitate, Eiji"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Inoue, Kirin"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sato, Retsushi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shimomoto, Youichi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ohba, Seigo"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ogata, Kinuko"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sakai, Tomoya"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ohno, Jun"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yamamoto, Ikuo"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Asahina, Izumi"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2022-08-18"}],"displaytype":"detail","filename":"DP17_62.pdf","filesize":[{"value":"1.5 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"DP17_62.pdf","url":"https://nagasaki-u.repo.nii.ac.jp/record/27529/files/DP17_62.pdf"},"version_id":"5356f2d0-2e2c-44da-9075-0e59ab14571f"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Oral cytology","subitem_subject_scheme":"Other"},{"subitem_subject":"Artificial intelligence","subitem_subject_scheme":"Other"},{"subitem_subject":"Sliding window method","subitem_subject_scheme":"Other"},{"subitem_subject":"Mask-RCNN","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology"}]},"item_type_id":"2","owner":"6","path":["11"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-08-18"},"publish_date":"2022-08-18","publish_status":"0","recid":"27529","relation_version_is_last":true,"title":["Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology"],"weko_creator_id":"6","weko_shared_id":-1},"updated":"2023-05-15T19:40:13.977068+00:00"}