Abstract: A clinical support system comprises a processor and a display component, wherein: the processor is configured to: receive image data, the image data representing an image of a plurality of cells obtained from a human or animal subject, the image data comprising a plurality of subsets of image data, each subset comprising data representing a portion of the image data corresponding to a respective cell of the plurality of cells; apply a trained deep learning neural network model to each subset of the image data, the deep learning neural network model comprising: a plurality of convolutional neural network layers each comprising a plurality of nodes; and a bottleneck layer comprising no more than ten nodes, wherein the processor is configured to apply the trained deep learning neural network model to each subset of the image data by applying the plurality of CNN layers, and subsequently applying the bottleneck layer, each node of the bottleneck layer of the machine-learning model configured to output a respectiv

Abstract: A computer-implemented method of generating training data to be used to train a machine learning model for generating a segmentation mask of an image containing overlapping particles. Training data is generated from sparse particle images which contain no overlaps. Generating masks for non-overlapping particles is generally not a problem if the particles can be identified clearly; in many cases simple methods such as thresholding already yield usable masks. The sparse images can then be combined to images which contain artificial overlaps. The same can be done for the masks as well which yields a large amount of training data, because of the many combinations which can be created from just a small set of images. The method is simple yet effective and can be adapted to many domains for example by adding style-transfer to the generated images or by including additional augmentation steps.

Abstract: A method of extracting linear features from an image, the method including the steps of: (a) applying a non maximum suppression filter to the image for different angles of response to produce a series of filtered image responses; (b) combining the filtered image responses into a combined image having extracted linear features.

Abstract: A method of extracting linear features from an image, the method including the steps of: (a) applying a non maximum suppression filter to the image for different angles of response to produce a series o filtered image responses; (b) combining the filtered image responses into a combined image having extracted linear features.