A method comprising: accessing image data representing tissue; identifying one or more features of the tissue indicated by the image data; selecting a model (30a-30e) for the tissue based on the one or more identified features; segmenting the image data; and identifying, using the model (30a-30e), one or more anatomical landmarks of the tissue indicated by the segmented image data (22, 42); wherein receiving the image data, identifying one or more features of the tissue, selecting a model (30a-30e), segmenting the image data, and identifying the one or more anatomical landmarks are performed by one or more computing devices; wherein the model (30a-30e) is selected from a plurality of reference models (30a-30e), each reference model (30a-30e) corresponding to a different population of patients; wherein each reference model (30a-30e) includes a set of attributes describing the corresponding patient population; and wherein selecting the model (30a-30e) for the tissue based on the one or more identified features includes selecting the reference model (30a-30e) based on a comparison of the one or more identified features with the set of attributes for each of the reference models (30a-30e), characterised in that the method further comprising determining that a region of the image data is likely to include a particular anatomical landmark based on the one or more identified features; and wherein identifying one or more anatomical landmarks in the segmented image data (22, 42) using the model (30a-30e) comprises: identifying a contour corresponding to the tissue of interest; identifying local minima and maxima on the contour; selecting at least one of the local maxima, local minima and a point of inflexion as candidate landmarks; calculating a distance between each candidate landmark and a corresponding reference landmark on the reference model (30a-30e); and designating the candidate landmark closest to the reference landmark as an anatomical landmark. accessing image data representing tissue; identifying one or more features of the tissue indicated by the image data; selecting a model (30a-30e) for the tissue based on the one or more identified features; segmenting the image data; and identifying, using the model (30a-30e), one or more anatomical landmarks of the tissue indicated by the segmented image data (22, 42); wherein receiving the image data, identifying one or more features of the tissue, selecting a model (30a-30e), segmenting the image data, and identifying the one or more anatomical landmarks are performed by one or more computing devices; wherein the model (30a-30e) is selected from a plurality of reference models (30a-30e), each reference model (30a-30e) corresponding to a different population of patients; wherein each reference model (30a-30e) includes a set of attributes describing the corresponding patient population; and wherein selecting the model (30a-30e) for the tissue based on the one or more identified features includes selecting the reference model (30a-30e) based on a comparison of the one or more identified features with the set of attributes for each of the reference models (30a-30e), characterised in that the method further comprising determining that a region of the image data is likely to include a particular anatomical landmark based on the one or more identified features; and wherein identifying one or more anatomical landmarks in the segmented image data (22, 42) using the model (30a-30e) comprises: identifying a contour corresponding to the tissue of interest; identifying local minima and maxima on the contour; selecting at least one of the local maxima, local minima and a point of inflexion as candidate landmarks; calculating a distance between each candidate landmark and a corresponding reference landmark on the reference model (30a-30e); and designating the candidate landmark closest to the reference landmark as an anatomical landmark. identifying a contour corresponding to the tissue of interest; identifying local minima and maxima on the contour; selecting at least one of the local maxima, local minima and a point of inflexion as candidate landmarks; calculating a distance between each candidate landmark and a corresponding reference landmark on the reference model (30a-30e); and designating the candidate landmark closest to the reference landmark as an anatomical landmark.