A computer implemented image processing method for determining the positions of specific parts of cells included in a test image (It) without a marker obtained by imaging (S101) the cells, the image processing method comprising: generating (S103) an intermediate image (Im) which corresponds to the test image (It) and in which a pseudo marker is administrated, by inputting (S102) the test image (It) to a first learning model ; and generating (S105) an image in which the position of each specific part is indicated by a representative point thereof by inputting (S104) the intermediate image to a second learning model and outputting the image as a result image (Io), wherein: the first learning model is constructed by using teacher data associating a first image (I1) and a second image (I2) captured to include a same cell and performing deep learning (S202) with the second image (I2) corresponding to an input and the first image (I1) corresponding to an output, the first image (I1) being an image in which the marker is expressed and the second image (I2) being an image in which the marker is not expressed; and the second learning model is constructed by using teacher data associating a third image (I3), 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 (I3) and performing deep learning (S304) with the third image (I3) corresponding to an input and the position of the representative point corresponding to an output. generating (S103) an intermediate image (Im) which corresponds to the test image (It) and in which a pseudo marker is administrated, by inputting (S102) the test image (It) to a first learning model ; and generating (S105) an image in which the position of each specific part is indicated by a representative point thereof by inputting (S104) the intermediate image to a second learning model and outputting the image as a result image (Io), wherein: the first learning model is constructed by using teacher data associating a first image (I1) and a second image (I2) captured to include a same cell and performing deep learning (S202) with the second image (I2) corresponding to an input and the first image (I1) corresponding to an output, the first image (I1) being an image in which the marker is expressed and the second image (I2) being an image in which the marker is not expressed; and the second learning model is constructed by using teacher data associating a third image (I3), 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 (I3) and performing deep learning (S304) with the third image (I3) corresponding to an input and the position of the representative point corresponding to an output. the first learning model is constructed by using teacher data associating a first image (I1) and a second image (I2) captured to include a same cell and performing deep learning (S202) with the second image (I2) corresponding to an input and the first image (I1) corresponding to an output, the first image (I1) being an image in which the marker is expressed and the second image (I2) being an image in which the marker is not expressed; and the second learning model is constructed by using teacher data associating a third image (I3), 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 (I3) and performing deep learning (S304) with the third image (I3) corresponding to an input and the position of the representative point corresponding to an output. <EOS>