Updated on 2024/09/19

写真a

 
IKEDA Katsuhide
 
Organization
Graduate School of Medicine Associate professor
Graduate School
Graduate School of Medicine
Undergraduate School
School of Health Sciences
Title
Associate professor
External link

Degree 1

  1. 博士(医学) ( 2014.1   昭和大学 ) 

Research Interests 3

  1. 免疫組織細胞化学

  2. 細胞診断検査学

  3. 人体病理学

Research Areas 2

  1. Life Science / Medical systems

  2. Life Science / Human pathology

Research History 5

  1. Nagoya University   Graduate School of Medicine   Associate professor

    2020.4

  2. 名古屋大学医学部   保健学科   准教授

    2020.4

      More details

  3. International University of Health and Welfare   Lecturer

    2016.4 - 2020.3

  4. 国立がん研究センター中央病院   病理・臨床検査科

    2011.5 - 2016.3

      More details

  5. 昭和大学藤が丘病院   中央臨床検査部・病理診断科

    2000.4 - 2011.4

      More details

Education 1

  1. 昭和大学大学院医学研究科

    2006.4 - 2014.1

      More details

Professional Memberships 14

  1. 日本臨床細胞学会東海連合会

    2020.4

  2. 愛知県臨床細胞学会

    2020.4

  3. 千葉県臨床細胞学会   理事

    2018.4 - 2020.3

  4. 千葉県臨床検査技師会   理事

    2017.5 - 2021.4

  5. 千葉県細胞検査士会   理事

    2016.4 - 2021.3

▼display all

Committee Memberships 3

  1. 日本臨床細胞学会   評議員  

    2021.6   

      More details

    Committee type:Academic society

    researchmap

  2. 千葉県臨床検査技師会   細胞診研究班  

    2018.5 - 2020.4   

      More details

  3. 千葉県臨床検査技師会   理事  

    2017.5 - 2020.4   

      More details

    Committee type:Academic society

 

Papers 45

  1. Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning. Reviewed International journal

    Katsuhide Ikeda, Nanako Sakabe, Chihiro Ito, Yuka Shimoyama, Kenta Toda, Kenta Fukuda, Yuma Yoshizaki, Shouichi Sato, Kohzo Nagata

    American journal of clinical pathology   Vol. 161 ( 4 ) page: 399 - 410   2024.4

     More details

    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    OBJECTIVES: Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general purpose deep learning-based cytology models. METHODS: Using 11 types of human cancer cell lines, we prepared Papanicolaou- and May-Grünwald-Giemsa (MGG)-stained specimens. We created deep learning models with different cell types, staining, and magnifications from each cell image using the You Only Look Once, version 8 (YOLOv8) algorithm. Detection and classification rates were calculated to compare the models. RESULTS: The classification rates of all the created models were over 95.9%. The highest detection rates of the Papanicolaou and MGG models were 92.3% and 91.3%, respectively. The highest detection rates of the object detection and instance segmentation models, which were 11 cell types with Papanicolaou staining, were 94.6% and 91.7%, respectively. CONCLUSIONS: We believe that the artificial intelligence technology of YOLOv8 has sufficient performance for applications in screening and cell classification in clinical settings. Conducting research to demonstrate the efficacy of YOLOv8 artificial intelligence technology on clinical specimens is crucial for overcoming the unique challenges associated with cytology.

    DOI: 10.1093/ajcp/aqad162

    Scopus

    PubMed

    researchmap

  2. Comparison of the effects of renal denervation at early or advanced stages of hypertension on cardiac, renal, and adipose tissue pathology in Dahl salt-sensitive rats. Reviewed

    Nagata K, Tagami K, Okuzawa T, Hayakawa M, Nomura A, Nishimura T, Ikeda K, Kitada K, Kobuchi S, Fujisawa Y, Nishiyama A, Murohara T

    Hypertension research : official journal of the Japanese Society of Hypertension     2024.2

     More details

    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Hypertension Research  

    Renal denervation (RDN) has emerged as a novel therapy for drug-resistant hypertension. We here examined the effects of RDN at early versus advanced stages of hypertension on blood pressure and organ pathology in rats with salt-sensitive hypertension. Dahl salt-sensitive (DahlS) rats fed an 8% NaCl diet from 6 weeks of age were subjected to RDN (surgical ablation and application of 10% phenol in ethanol) or sham surgery at 7 (early stage) or 9 (advanced stage) weeks and were studied at 12 weeks. RDN at early or advanced stages resulted in a moderate lowering of blood pressure. Although RDN at neither stage affected left ventricular (LV) and cardiomyocyte hypertrophy, it ameliorated LV diastolic dysfunction, fibrosis, and inflammation at both stages. Intervention at both stages also attenuated renal injury as well as downregulated the expression of angiotensinogen and angiotensin-converting enzyme (ACE) genes and angiotensin II type 1 receptor protein in the kidney. Furthermore, RDN at both stages inhibited proinflammatory gene expression in adipose tissue. The early intervention reduced both visceral fat mass and adipocyte size in association with downregulation of angiotensinogen and ACE gene expression. In contrast, the late intervention increased fat mass without affecting adipocyte size as well as attenuated angiotensinogen and ACE gene expression. Our results thus indicate that RDN at early or late stages after salt loading moderately alleviated hypertension and substantially ameliorated cardiac and renal injury and adipose tissue inflammation in DahlS rats. They also suggest that cross talk among the kidney, cardiovascular system, and adipose tissue may contribute to salt-sensitive hypertension. (Figure presented.)

    DOI: 10.1038/s41440-024-01605-x

    Scopus

    PubMed

  3. Effect of liquid‐based cytology fixing solution on immunocytochemistry: Efficacy of antigen retrieval in cytologic specimens Reviewed

    Nanako Sakabe, Sayumi Maruyama, Chihiro Ito, Yuka Shimoyama, Kazuhisa Sudo, Shouichi Sato, Katsuhide Ikeda

    Diagnostic Cytopathology   Vol. 51 ( 9 ) page: 546 - 553   2023.9

     More details

    Authorship:Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    Abstract

    Background

    Immunocytochemistry (ICC) is an indispensable technique to improve diagnostic accuracy. ICC using liquid‐based cytology (LBC)‐fixed specimens has been reported. However, problems may arise if the samples are not fixed appropriately. We investigated the relationship between the LBC fixing solution and ICC and the usefulness of antigen retrieval (AR) in LBC specimens.

    Methods

    Specimens were prepared from five types of LBC‐fixed samples using cell lines and the SurePath™ method. ICC was performed using 13 antibodies and analyzed by counting the number of positive cells in the immunocytochemically stained specimens.

    Results

    Insufficient reactivity was observed using ICC without heat‐induced AR (HIAR) in nuclear antigens. The number of positive cells increased in ICC with HIAR. The percentage of positive cells was lower in CytoRich™ Blue samples for Ki‐67 and in CytoRich™ Red and TACAS™ Ruby samples for estrogen receptor and p63 than in the other samples. For cytoplasmic antigens, the percentage of positive cells for no‐HIAR treatment specimens was low in the three antibodies used. In cytokeratin 5/6, the number of positive cells increased in all LBC specimens with HIAR, and the percentage of positive cells in CytoRich™ Red and TACAS™ Ruby samples was significantly lower (p < .01). For cell membrane antigens, CytoRich™ Blue samples had a lower percentage of positive cells than the other LBC‐fixed samples.

    Conclusion

    The combination of detected antigen, used cells, and fixing solution may have different effects on immunoreactivity. ICC using LBC specimens is a useful technique, but the staining conditions should be examined before performing ICC.

    DOI: 10.1002/dc.25178

    Scopus

    PubMed

    researchmap

  4. Relationship between a deep learning model and liquid‐based cytological processing techniques Reviewed

    Katsuhide Ikeda, Nanako Sakabe, Sayumi Maruyama, Chihiro Ito, Yuka Shimoyama, Wataru Oboshi, Tetsuya Komene, Yoshitaka Yamaguchi, Shouichi Sato, Kohzo Nagata

    Cytopathology   Vol. 34 ( 4 ) page: 308 - 317   2023.7

     More details

    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Wiley  

    Abstract

    Objective

    Artificial intelligence (AI)–based cytopathology studies conducted using deep learning have enabled cell detection and classification. Liquid‐based cytology (LBC) has facilitated the standardisation of specimen preparation; however, cytomorphology varies according to the LBC processing technique used. In this study, we elucidated the relationship between two LBC techniques and cell detection and classification using a deep learning model.

    Methods

    Cytological specimens were prepared using the ThinPrep and SurePath methods. The accuracy of cell detection and cell classification was examined using the one‐ and five‐cell models, which were trained with one and five cell types, respectively.

    Results

    When the same LBC processing techniques were used for the training and detection preparations, the cell detection and classification rates were high. The model trained on ThinPrep preparations was more accurate than that trained on SurePath. When the preparation types used for training and detection were different, the accuracy of cell detection and classification was significantly reduced (P < 0.01). The model trained on both ThinPrep and SurePath preparations exhibited slightly reduced cell detection and classification rates but was highly accurate.

    Conclusions

    For the two LBC processing techniques, cytomorphology varied according to cell type; this difference affects the accuracy of cell detection and classification by deep learning. Therefore, for highly accurate cell detection and classification using AI, the same processing technique must be used for both training and detection. Our assessment also suggests that a deep learning model should be constructed using specimens prepared via a variety of processing techniques to construct a globally applicable AI model.

    DOI: 10.1111/cyt.13235

    Scopus

    PubMed

    researchmap

  5. Effect of Specimen Processing Technique on Cell Detection and Classification by Artificial Intelligence Reviewed

    Sayumi Maruyama, Nanako Sakabe, Chihiro Ito, Yuka Shimoyama, Shouichi Sato, Katsuhide Ikeda

    American Journal of Clinical Pathology   Vol. 159 ( 5 ) page: 448 - 454   2023.5

     More details

    Authorship:Last author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)   Publisher:Oxford University Press (OUP)  

    Abstract

    Objectives

    Cytomorphology is known to differ depending on the processing technique, and these differences pose a problem for automated diagnosis using deep learning. We examined the as-yet unclarified relationship between cell detection or classification using artificial intelligence (AI) and the AutoSmear (Sakura Finetek Japan) and liquid-based cytology (LBC) processing techniques.

    Methods

    The “You Only Look Once” (YOLO), version 5x, algorithm was trained on the AutoSmear and LBC preparations of 4 cell lines: lung cancer (LC), cervical cancer (CC), malignant pleural mesothelioma (MM), and esophageal cancer (EC). Detection and classification rates were used to evaluate the accuracy of cell detection.

    Results

    When preparations of the same processing technique were used for training and detection in the 1-cell (1C) model, the AutoSmear model had a higher detection rate than the LBC model. When different processing techniques were used for training and detection, detection rates of LC and CC were significantly lower in the 4-cell (4C) model than in the 1C model, and those of MM and EC were approximately 10% lower in the 4C model.

    Conclusions

    In AI-based cell detection and classification, attention should be paid to cells whose morphologies change significantly depending on the processing technique, further suggesting the creation of a training model.

    DOI: 10.1093/ajcp/aqac178

    Scopus

    PubMed

    researchmap

▼display all

Books 7

  1. クエスチョン・バンク 臨床検査技師2025

    ( Role: Contributor ,  病理・細胞診)

    MEDIC MEDIA  2024.5  ( ISBN:978-4-89632-912-4

     More details

    Language:Japanese Book type:Textbook, survey, introduction

  2. クエスチョン・バンク 臨床検査技師2023-24

    ( Role: Contributor ,  病理・細胞診)

    MEDIC MEDIA  2023.5  ( ISBN:978-4-89632-912-4

     More details

    Language:Japanese Book type:Textbook, survey, introduction

  3. 染色法のすべて

    ( Role: Contributor ,  レフレルのメチレン青染色)

    医歯薬出版  2021.3 

     More details

    Language:Japanese Book type:Textbook, survey, introduction

    researchmap

  4. 染色法のすべて

    水口, 國雄( Role: Contributor ,  レフレルのメチレン青染色)

    医歯薬出版  2021.3  ( ISBN:9784263226902

     More details

    Total pages:xxiv, 446p   Language:Japanese

    CiNii Books

    researchmap

  5. 組織細胞化学

    日本組織細胞化学会( Role: Contributor ,  免疫染色の応用 -多重染色法-)

    学際企画  2019.7 

     More details

    Total pages:冊   Language:Japanese

    CiNii Books

    researchmap

▼display all

MISC 16

  1. ゲノムDNAにおける簡易抽出法の開発について

    橋本 優佑, 米根 鉄矢, 大星 航, 池田 勝秀, 山口 良考

    国際医療福祉大学学会誌   Vol. 25 ( 抄録号 ) page: 118 - 118   2020.11

     More details

    Language:Japanese   Publisher:国際医療福祉大学学会  

    researchmap

  2. 複数生じたPCRバンドの簡易シーケンス法

    中村 蓮, 北村 浩一, 京都 敬祐, 藤井 樹, 岩淵 由起, 橋本 優佑, 米根 鉄矢, 池田 勝秀, 大星 航, 平田 雄哉, 飯塚 信義, 古賀 豊大, 田嶋 明彦, 山口 良考

    生物試料分析   Vol. 43 ( 3 ) page: 200 - 207   2020.6

     More details

    Language:Japanese   Publisher:(NPO)生物試料分析科学会  

    ゲノムDNA由来上皮成長因子受容体(EGFR)遺伝子とcDNA由来Siglec-E遺伝子を対象に、複数生じたPCR産物の塩基配列を確認する簡易法について報告した。健常人のヒト口腔細胞より、NucleoSpin DNA RapidLyseを用いてゲノムDNAを抽出した。Aire+細胞株由来の1st strand cDNAはRNAiso Plusにてtotal RNAを抽出し、逆転写酵素SuperScript IV Reverse Transcriptaseを用いて合成した。バンド1由来PCR産物では、解読された塩基配列を相同性検索サイト「NCBI/nucleotide BLAST Search」にて検索した。その結果、ヒト7番染色体に座位するEGFRのRefseqGeneが抽出され、目的産物であることが確認された。バンド3も同様に目的産物であることが確認された。バンド2のシーケンスでは複数の波形が雑多に観察され、BLAST SearchではNo Significant Similarity Foundと相同性のある配列は検出されなかった。バンド5由来PCR産物のシーケンスデータでは、Siglec-EのmRNAが抽出され目的産物であることが確認された。本法は低電圧・長時間でのDNA断片の分離操作が必要ではあるが、特殊な装置を必要とせず、汎用試薬で複数のPCRバンドの塩基配列を決定できると考えられた。

    researchmap

    Other Link: https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2020&ichushi_jid=J01925&link_issn=&doc_id=20200709020007&doc_link_id=%2Fcu8analy%2F2020%2F004303%2F008%2F0200-0207%26dl%3D0&url=http%3A%2F%2Fwww.medicalonline.jp%2Fjamas.php%3FGoodsID%3D%2Fcu8analy%2F2020%2F004303%2F008%2F0200-0207%26dl%3D0&type=MedicalOnline&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00004_2.gif

  3. 臨床検査カレッジ:細胞学的検討(2)神経芽腫群腫瘍の細胞学的検討

    福留伸幸, 池田勝秀, 水口國雄

    医療と検査機器・試薬   Vol. 37 ( 6 ) page: 741 - 752   2014.12

     More details

    Language:Japanese  

    J-GLOBAL

    researchmap

  4. 肺癌の検査・診断 検査・診断 病理検査・診断 液状検体からのセルブロック作製の意義と方法

    池田勝秀, 蔦幸治

    日本臨床   Vol. 71 ( 増刊6 最新肺癌学 ) page: 404 - 407   2013.11

     More details

    Language:Japanese  

    J-GLOBAL

    researchmap

  5. 免疫組織化学多重染色法(A.染める!-免疫染色法の基礎と応用-,組織細胞化学の挑戦-臨床応用研究への飛躍)

    鈴木 孝夫, 池田 勝秀, 柳田 絵美衣

    組織細胞化学   Vol. 2012   page: 35 - 47   2012.7

     More details

    Language:Japanese   Publisher:日本組織細胞化学会  

    CiNii Books

    researchmap

▼display all

Presentations 46

  1. 細胞画像を用いた物体検出とインスタンスセグメンテーション

    福田健太・戸田健太・吉崎友真・坂部名奈子・池田勝秀

    第65回日本臨床細胞学会春期大会  2024.6.8  日本臨床細胞学会

     More details

    Event date: 2024.6

    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:大阪   Country:Japan  

  2. 深層学習YOLOv7xにおける学習細胞数およ及び撮影倍率の違いとAI細胞検出精度の関係性

    福田健太・伊藤千尋・下山優香・坂部名奈子・池田勝秀

    第62回日本臨床細胞学会秋季大会  2023.11.4  日本臨床細胞学会

     More details

    Event date: 2023.11

    Language:Japanese   Presentation type:Poster presentation  

    Venue:福岡   Country:Japan  

  3. LBC保存液とAIの関係:YOLOv5深層CNNを用いたLBC標本細胞検出

    2023.6.10 

     More details

    Event date: 2023.6

    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  4. LBC標本作製法とディープラーニングモデルの関係性

    下山優香・伊藤千尋・坂部名奈子・池田勝秀

    第64回日本臨床細胞学会総会  2023.6.10  日本臨床細胞学会

     More details

    Event date: 2023.6

    Language:Japanese   Presentation type:Oral presentation (general)  

    Venue:名古屋   Country:Japan  

  5. 【呼吸器】セルブロック Invited

    池田勝秀

    第59回日本臨床細胞学会総会  2020.11.21  日本臨床細胞学会

     More details

    Event date: 2020.11

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

    Venue:神奈川   Country:Japan  

▼display all

KAKENHI (Grants-in-Aid for Scientific Research) 1

  1. 体腔液細胞診におけるAI診断の開発-細胞像変化への対応と標本作製技術の検討-

    Grant number:21K18077  2021.4 - 2025.3

    日本学術振興会  科学研究費助成事業  若手研究

    池田勝秀

      More details

    Authorship:Principal investigator  Grant type:Competitive

    Grant amount:\4680000 ( Direct Cost: \3600000 、 Indirect Cost:\1080000 )

 

Teaching Experience (On-campus) 31

  1. Pathology

    2021

  2. Anatomy II

    2021

  3. Anatomy I

    2021

  4. 基礎セミナーA

    2021

  5. 生物学実験

    2021

▼display all

Teaching Experience (Off-campus) 6

  1. 病理学

    2020.4 名古屋大学医学部保健学科)

     More details

  2. 細胞診断検査学、細胞診断検査学実習

    2020.4 名古屋大学医学部保健学科)

     More details

  3. 病理検査学、病理検査学実習

    2020.4 名古屋大学医学部保健学科)

     More details

  4. 組織学、組織学実習

    2020.4 名古屋大学医学部保健学科)

     More details

  5. 病理検査学、実習

    2016.4 - 2020.3 国際医療福祉大学成田保健医療学部)

     More details

▼display all