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1. 发明申请

US20220404258A1 AUTOMATIC CALIBRATION 有权
公开(公告)号：US20220404258A1
公开(公告)日：2022-12-22
申请号：US17834822
申请日：2022-06-07
申请人： SOLENTIM LTD.
发明人： Yonggang JIANG
IPC分类号： G01N15/10 , G01N33/58 , G01N15/14
摘要： A calibration apparatus comprises estimation circuitry configured to estimate, based on a calibration factor, an estimated number of cells of a first type in a dyed biological sample containing an unknown number of cells. Determination circuitry determines the actual number of cells of the first type in the dyed biological sample. Processing circuitry adjusts the calibration factor. The estimation circuitry is configured with the processing circuitry to estimate the estimated number of the cells of the first type in the dyed biological sample one or more times, based on a different value of the calibration factor for each of the one or more times, until the estimated number of the cells of the first type approaches the actual number of cells of the first type.

2. 发明申请

US20220406080A1 AUTOMATIC CALIBRATION USING MACHINE LEARNING 有权
公开(公告)号：US20220406080A1
公开(公告)日：2022-12-22
申请号：US17834803
申请日：2022-06-07
申请人： SOLENTIM LTD.
发明人： Yonggang JIANG
IPC分类号： G06V20/69 , G06T7/00
摘要： There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

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