Abstract: A method (900) of generating an atlas for a universal atlas database (901) is provided. A new medical scan image (905) is provided. A universal auto-contouring operation (920) is performed on the medical scan image, to generate a set of universal contours (930) for the medical scan image. A local auto-contouring customisation operation (940) is performed on the medical scan image, to generate a set of local contours (950) for the medical scan image. The set of local contours is standardised (980) using a trained model to compensate for biases in the set of local contours, thereby creating a set of standardised global contours (985) for the medical scan image. The set of standardised global contours (985) and the medical scan image (905) can be added to the universal atlas database (901) as a new atlas, thereby expanding the set of atlases that are available in the universal atlas database.

Abstract: A method (900) of generating an atlas for a universal atlas database (901) is provided. A new medical scan image (905) is provided. A universal auto-contouring operation (920) is performed on the medical scan image, to generate a set of universal contours (930) for the medical scan image. A local auto-contouring customisation operation (940) is performed on the medical scan image, to generate a set of local contours (950) for the medical scan image. The set of local contours is standardised (980) using a trained model to compensate for biases in the set of local contours, thereby creating a set of standardised global contours (985) for the medical scan image. The set of standardised global contours (985) and the medical scan image (905) can be added to the universal atlas database (901) as a new atlas, thereby expanding the set of atlases that are available in the universal atlas database.

Abstract: A method of processing medical images, performed by a computer processor and comprising the steps of: (a) obtaining one or more atlases containing one or more images in which one or more anatomical features have been labelled with label data; (b) obtaining a plurality of unlabelled images; (c) comparing the labelled and unlabelled images and selecting one or more unlabelled images that most closely resemble(s) one or more of the labelled images; (d) to each of those selected image(s), propagating label data from one or more of the closest of the labelled images, thereby labelling the corresponding anatomical feature(s) of each of the selected image(s) and causing the selected image(s) to become labelled image(s); and (e) iteratively repeating from step (c), thereby labelling others of the unlabelled images.