A method of segmenting a 3D object in a medical radiation image represented by a number of 2D slice images comprising the steps of a) Displaying at least two of said slice images, b) Manually drawing a contour line delineating a region of interest pertaining to said 3D object in the displayed 2D slice images, c) Feeding pixels of the drawn contour line into a signal processing device running a fitting algorithm and fitting the drawn contour line to the object by applying said fitting algorithm to said pixels, d) Generating segmentation masks by segmenting in each of said slice images a 2D object based on pixels of the fitted contour computed in step c, e) Performing interpolation on said segmentation masks so as to generate a segmented 3D object, characterized in that the interpolation on the segmentation masks is performed by - computing the 3D convex hull containing all pixels classified as inside the object in the segmentation mask, and - refining said convex hill to yield a final segmentation mask by iteratively performing the following steps within the convex hull mask - 1) Find a set V of candidate voxels, being all voxels in the convex hull not yet classified as inside or outside and adjacent to at least one voxel already classified as inside or outside, - 2) Classify each voxel v in V in or out the final segmentation object as follow: - if the number of adjacent and already classified voxels is exactly one then v gets the same type as this neighbour; - Otherwise v gets the type of a neighbour with the most similar intensity value, - 3) If all voxels in the convex hull mask are classified end the algorithm; otherwise, go to step 1. a) Displaying at least two of said slice images, b) Manually drawing a contour line delineating a region of interest pertaining to said 3D object in the displayed 2D slice images, c) Feeding pixels of the drawn contour line into a signal processing device running a fitting algorithm and fitting the drawn contour line to the object by applying said fitting algorithm to said pixels, d) Generating segmentation masks by segmenting in each of said slice images a 2D object based on pixels of the fitted contour computed in step c, e) Performing interpolation on said segmentation masks so as to generate a segmented 3D object, characterized in that the interpolation on the segmentation masks is performed by - computing the 3D convex hull containing all pixels classified as inside the object in the segmentation mask, and - refining said convex hill to yield a final segmentation mask by iteratively performing the following steps within the convex hull mask - 1) Find a set V of candidate voxels, being all voxels in the convex hull not yet classified as inside or outside and adjacent to at least one voxel already classified as inside or outside, - 2) Classify each voxel v in V in or out the final segmentation object as follow: - if the number of adjacent and already classified voxels is exactly one then v gets the same type as this neighbour; - Otherwise v gets the type of a neighbour with the most similar intensity value, - 3) If all voxels in the convex hull mask are classified end the algorithm; otherwise, go to step 1. - computing the 3D convex hull containing all pixels classified as inside the object in the segmentation mask, and - refining said convex hill to yield a final segmentation mask by iteratively performing the following steps within the convex hull mask - 1) Find a set V of candidate voxels, being all voxels in the convex hull not yet classified as inside or outside and adjacent to at least one voxel already classified as inside or outside, - 2) Classify each voxel v in V in or out the final segmentation object as follow: - if the number of adjacent and already classified voxels is exactly one then v gets the same type as this neighbour; - Otherwise v gets the type of a neighbour with the most similar intensity value, - 3) If all voxels in the convex hull mask are classified end the algorithm; otherwise, go to step 1. - 1) Find a set V of candidate voxels, being all voxels in the convex hull not yet classified as inside or outside and adjacent to at least one voxel already classified as inside or outside, - 2) Classify each voxel v in V in or out the final segmentation object as follow: - if the number of adjacent and already classified voxels is exactly one then v gets the same type as this neighbour; - Otherwise v gets the type of a neighbour with the most similar intensity value, - if the number of adjacent and already classified voxels is exactly one then v gets the same type as this neighbour; - Otherwise v gets the type of a neighbour with the most similar intensity value, - 3) If all voxels in the convex hull mask are classified end the algorithm; otherwise, go to step 1.