An image analysis method for analyzing an image of a tissue (308, 408) or a cell using a deep learning algorithm of a neural network structure, the method comprising: generating analysis data from an analysis target image that includes the tissue (308, 408) or the cell to be analyzed; inputting the analysis data to the deep learning algorithm; and generating data indicating a region of a cell nucleus in the analysis target image by the deep learning algorithm, wherein: the deep learning algorithm is generated from training data including pairs of first training data (72r, 72g, 72b) and second training data, the first training data (72r, 72g, 72b) includes a color density value data of each pixel generated from a bright field image of a tissue or a cell which is stained for bright field observation, the second training data includes a binarized data of each pixel generated from a fluorescence image of a same or corresponding region of the tissue or the cell whose nucleus is stained for florescence observation, wherein the binarized data indicates whether each pixel is a region of a cell nucleus, and the deep learning algorithm learns a relationship between the first training data (72r, 72g, 72b), as an input layer (50a, 60a) of the neural network structure, and the second training data, as an output layer (50b, 60c) of the neural network structure. generating analysis data from an analysis target image that includes the tissue (308, 408) or the cell to be analyzed; inputting the analysis data to the deep learning algorithm; and generating data indicating a region of a cell nucleus in the analysis target image by the deep learning algorithm, wherein: the deep learning algorithm is generated from training data including pairs of first training data (72r, 72g, 72b) and second training data, the first training data (72r, 72g, 72b) includes a color density value data of each pixel generated from a bright field image of a tissue or a cell which is stained for bright field observation, the second training data includes a binarized data of each pixel generated from a fluorescence image of a same or corresponding region of the tissue or the cell whose nucleus is stained for florescence observation, wherein the binarized data indicates whether each pixel is a region of a cell nucleus, and the deep learning algorithm learns a relationship between the first training data (72r, 72g, 72b), as an input layer (50a, 60a) of the neural network structure, and the second training data, as an output layer (50b, 60c) of the neural network structure. the deep learning algorithm is generated from training data including pairs of first training data (72r, 72g, 72b) and second training data, the first training data (72r, 72g, 72b) includes a color density value data of each pixel generated from a bright field image of a tissue or a cell which is stained for bright field observation, the second training data includes a binarized data of each pixel generated from a fluorescence image of a same or corresponding region of the tissue or the cell whose nucleus is stained for florescence observation, wherein the binarized data indicates whether each pixel is a region of a cell nucleus, and the deep learning algorithm learns a relationship between the first training data (72r, 72g, 72b), as an input layer (50a, 60a) of the neural network structure, and the second training data, as an output layer (50b, 60c) of the neural network structure.