Title:
CELL ANALYSIS METHOD, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS DEVICE, TRAINING METHOD FOR DEEP LEARNING ALGORITHM, CELL ANALYSIS PROGRAM, AND TRAINING PROGRAM FOR DEEP LEARNING ALGORITHM
Document Type and Number:
WIPO Patent Application WO/2020/196074
Kind Code:
A1
Abstract:
The present invention determines a cell type that has not been able to be determined in a conventional scattergram. The present invention addresses an issue by means of a cell analysis method, which analyzes a cell included in a biological sample by using a deep learning algorithm of a neural network structure, the method including: flowing the cell to a passage; acquiring a signal strength pertaining to each cell passing in the passage; inputting, to a deep learning algorithm, numerical data corresponding to the acquired signal strength pertaining to each cell; and determining, on the basis of an output result from the deep learning algorithm for each cell, the type of the cell for which the signal strength is acquired for each cell.
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Inventors:
KIMURA KONOBU (JP)
TANAKA MASAMICHI (JP)
ASADA SHOICHIRO (JP)
TANAKA MASAMICHI (JP)
ASADA SHOICHIRO (JP)
Application Number:
PCT/JP2020/011596
Publication Date:
October 01, 2020
Filing Date:
March 17, 2020
Export Citation:
Assignee:
SYSMEX CORP (JP)
International Classes:
G01N33/48; C12M1/34; C12Q1/04; G01N33/49
Domestic Patent References:
| WO2018203568A1 | 2018-11-08 | |||
| WO2017073737A1 | 2017-05-04 | |||
| WO2018039216A1 | 2018-03-01 | |||
| WO2018203568A1 | 2018-11-08 |
Foreign References:
| JP2014106059A | 2014-06-09 | |||
| JP2016186463A | 2016-10-27 | |||
| JP2001174456A | 2001-06-29 | |||
| US20180286038A1 | 2018-10-04 | |||
| JP2017219479A | 2017-12-14 | |||
| US20170052106A1 | 2017-02-23 | |||
| JPS63180836A | 1988-07-25 | |||
| US5891733A | 1999-04-06 | |||
| EP1136563A2 | 2001-09-26 | |||
| US7309581B2 | 2007-12-18 |
Other References:
ROOPA B.HEGDE , KEERTHANA PRASAD , HARISHCHANDRA HEBBAR , BRIJ MOHAN KUMA SINGH: "Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images", BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, vol. 39, no. 2, 1 April 2019 (2019-04-01), pages 382 - 392, XP055856156, ISSN: 0208-5216, DOI: 10.1016/j.bbe.2019.01.005
ROY, R. ET AL.: "Classification of WBC using deep learning for diagnosing diseases", PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT) 2018, 27 September 2018 (2018-09-27), pages 1634 - 1638, XP033408960, DOI: 10.1109/ICICCT.2018.8473028
SHAFIQUE, S. ET AL.: "Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep covolutional neural networks", TECHNOLOGY IN CANCER RESEARCH & TREATMENT, vol. 17, 27 September 2018 (2018-09-27), XP055743559, DOI: 10.1177/1533033818802789
TOMOHIKO AI, YOKO TABE, HIROYUKI TAKEMURA, KONOBU KIMURA, TOSHIHIRO TAKAHASHI, HAEUN YANG, KOJI TSUCHIYA, AYA KONISHI, KINYA UCHIH: "Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer", PLOS ONE, vol. 13, no. 2, 9 February 2018 (2018-02-09), pages e0190886, XP055743562, DOI: 10.1371/journal.pone.0190886
BARTEK RAJWA; MURUGESAN VENKATAPATHI; KATHY RAGHEB; PADMAPRIYA P BANADA; DANIEL HIRLEMAN E; TODD LARY; PAUL ROBINSON J: "Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier", CYTOMETRY PART A, vol. 73A, no. 4, 28 December 2007 (2007-12-28), pages 369 - 379, XP002648550, DOI: 10.1002/cyto.a.20515
KIMURA, KONOBU ET AL.: "A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA", SICENTIFIC REPORTS, vol. 9, no. 1, 16 September 2019 (2019-09-16), pages 13385, XP055743568, DOI: 10.1038/s41598-019-49942-z
KIMURA, KONOBU: "Development and validation of a Deep Learning Algorithm for detection of abnormal morphology of WBC", MD ANDERSON CANCER CENTER GLOBAL ACADEMIC PROGRAM (GAP) CONFERENCE, POSTER WS17, Retrieved from the Internet
See also references of EP 3943933A4
ROY, R. ET AL.: "Classification of WBC using deep learning for diagnosing diseases", PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT) 2018, 27 September 2018 (2018-09-27), pages 1634 - 1638, XP033408960, DOI: 10.1109/ICICCT.2018.8473028
SHAFIQUE, S. ET AL.: "Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep covolutional neural networks", TECHNOLOGY IN CANCER RESEARCH & TREATMENT, vol. 17, 27 September 2018 (2018-09-27), XP055743559, DOI: 10.1177/1533033818802789
TOMOHIKO AI, YOKO TABE, HIROYUKI TAKEMURA, KONOBU KIMURA, TOSHIHIRO TAKAHASHI, HAEUN YANG, KOJI TSUCHIYA, AYA KONISHI, KINYA UCHIH: "Novel flowcytometry-based approach of malignant cell detection in body fluids using an automated hematology analyzer", PLOS ONE, vol. 13, no. 2, 9 February 2018 (2018-02-09), pages e0190886, XP055743562, DOI: 10.1371/journal.pone.0190886
BARTEK RAJWA; MURUGESAN VENKATAPATHI; KATHY RAGHEB; PADMAPRIYA P BANADA; DANIEL HIRLEMAN E; TODD LARY; PAUL ROBINSON J: "Automated classification of bacterial particles in flow by multiangle scatter measurement and support vector machine classifier", CYTOMETRY PART A, vol. 73A, no. 4, 28 December 2007 (2007-12-28), pages 369 - 379, XP002648550, DOI: 10.1002/cyto.a.20515
KIMURA, KONOBU ET AL.: "A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA", SICENTIFIC REPORTS, vol. 9, no. 1, 16 September 2019 (2019-09-16), pages 13385, XP055743568, DOI: 10.1038/s41598-019-49942-z
KIMURA, KONOBU: "Development and validation of a Deep Learning Algorithm for detection of abnormal morphology of WBC", MD ANDERSON CANCER CENTER GLOBAL ACADEMIC PROGRAM (GAP) CONFERENCE, POSTER WS17, Retrieved from the Internet
See also references of EP 3943933A4
Attorney, Agent or Firm:
OSAKA Masahiro (JP)
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