A method for segmenting medical image sets and updating a knowledge-base by using a statistical model as a seed, comprising: - generating a sparse landmark signature set 114 of a present patient medical image set 112, - selecting reference image sets 102 from the knowledge-base 100 based upon their similarities to the present patient medical image set 112 meta-data and the sparse landmark signature set 114, whereas the selected reference image sets 102 form a base set 116, characterized by - deforming the base set 116 to the sparse landmark signature 114 of said medical image set 112 to create a statistical model 118 containing segmentation and contouring of the structures of interest in said medical image set 112, whereas contours of structures of interest are traced out by hand or the structures of interest are identified with structure-specific automatic contouring and/or automatic segmentation software programs, - using the statistical model 118 with its weighted-average segmentation of each structure of interest as a seed, the seed being provided by the identified structures of interest from the statistical model 118, - calculating the segmentation of one or more structures of interest in said medical image set 112 by using an automatic segmentation tool 130 using the seed provided by the statistical model 118, - creating an updated statistical model 120 when the segmentation results are returned by the automatic segmentation tool 130, whereas the updated statistical model 120 comprises weighted-average segmentation of each structure of interest in said medical image set 112, - adding the updated statistical model 120 to the knowledge-base 100 as a new reference image set 102. - generating a sparse landmark signature set 114 of a present patient medical image set 112, - selecting reference image sets 102 from the knowledge-base 100 based upon their similarities to the present patient medical image set 112 meta-data and the sparse landmark signature set 114, whereas the selected reference image sets 102 form a base set 116, characterized by whereas the selected reference image sets 102 form a base set 116, characterized by - deforming the base set 116 to the sparse landmark signature 114 of said medical image set 112 to create a statistical model 118 containing segmentation and contouring of the structures of interest in said medical image set 112, whereas contours of structures of interest are traced out by hand or the structures of interest are identified with structure-specific automatic contouring and/or automatic segmentation software programs, - using the statistical model 118 with its weighted-average segmentation of each structure of interest as a seed, the seed being provided by the identified structures of interest from the statistical model 118, - calculating the segmentation of one or more structures of interest in said medical image set 112 by using an automatic segmentation tool 130 using the seed provided by the statistical model 118, - creating an updated statistical model 120 when the segmentation results are returned by the automatic segmentation tool 130, whereas the updated statistical model 120 comprises weighted-average segmentation of each structure of interest in said medical image set 112, - adding the updated statistical model 120 to the knowledge-base 100 as a new reference image set 102. <EOS>