IMAGE PROCESSING METHOD, MEDICINE SENSITIVITY TEST METHOD AND IMAGE PROCESSING DEVICE

An image processing method of the invention is, to enable automatically measure a position and a number of cells included in an image even if a marker is not used, configured to input a test image to a first learning model (Step S102), input an output image to a second learning model (Step S104) and...

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Bibliographische Detailangaben
Hauptverfasser:	KISHIMOTO, Katsumi, MIZUKAMI, Tamio
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	APPARATUS FOR ENZYMOLOGY OR MICROBIOLOGY BEER BIOCHEMISTRY CALCULATING CHEMISTRY COMPOSITIONS OR TEST PAPERS THEREFOR COMPUTING CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES COUNTING ENZYMOLOGY HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS METALLURGY MICROBIOLOGY MUTATION OR GENETIC ENGINEERING PHYSICS PROCESSES OF PREPARING SUCH COMPOSITIONS SPIRITS TESTING VINEGAR WINE
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Beschreibung
Zusammenfassung:	An image processing method of the invention is, to enable automatically measure a position and a number of cells included in an image even if a marker is not used, configured to input a test image to a first learning model (Step S102), input an output image to a second learning model (Step S104) and output an output image as a result image in which the position of the detected part is indicated by a representative point (Step S105). The first learning model is constructed by deep learning using teacher data associating a first image in which a marker is expressed and a second image in which the marker is not expressed, the first and the second images being captured to include a same cell. The second learning model is constructed by deep learning using teacher data associating a third image, which is captured to include a cell and in which the marker is expressed, and information representing a position of the representative point included in the third image.