Abstract: An image processing method is configured to input a test image to a first learning model, input an output image to a second learning model and output an output image as a result image in which the position of the detected part is indicated by a representative point. 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 are 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.

Abstract: A technique for automatically and accurately determining life or death of cells in a sample prepared without any labeling, not relying on the subjectivity of a determiner, is intended. A fluorescent specimen in which respective fluorescence generation modes in response to excitation light are different from each other between a living cell and a dead cell is prepared, and fluorescence imaging of the fluorescent specimen is performed. Bright field imaging of the fluorescent specimen is performed, and respective positions of the living cell and the dead cell are specified from a fluorescent image. Image objects in a bright field image, which are located at positions corresponding to the specified positions of the living cell and the dead cell are extracted as a living cell object and a dead cell object, respectively, and a classifier is constructed through machine learning using supervisor data including respective feature vectors of the extracted objects.