Computer-implemented method for segmenting a tooth's pulp region from a 2D or 3D image of said tooth, wherein said pulp region comprises a pulp chamber and one or more root canals, the method comprising: performing a curvature-based shape recognition at different spatial scales using smoothed versions of the image; receiving an indication of a point or region assumed to be located in the imaged pulp chamber of the image and corresponding to at least one image element of the image, the at least one image element being hereinafter referred to as "seed"; using at least the seed as initial segmentation mask; and iteratively carrying out a procedure, hereinafter referred to as "update procedure", comprising: determining candidate image elements for updating the segmentation mask, wherein determining comprises: in at least the first n iteration(s) of the update procedure, a grayscale thresholding taking as reference the current segmentation mask said grayscale thresholding comprising determining the differences between the intensity level of an image element under consideration and an averaged foreground and background intensity value, respectively, wherein the averaged foreground value is computed as the average intensity value of image elements comprised in the current segmentation mask and wherein the averaged background intensity value is computed as the average intensity value of image elements outside the current segmentation mask, wherein n is a nonzero positive integer; and, in at least one iteration of the update procedure, taking the curvature-based shape recognition into account; retaining, among the candidate image elements, a region of connected candidate image elements, the region comprising the seed; and using the retained region to update the segmentation mask. performing a curvature-based shape recognition at different spatial scales using smoothed versions of the image; receiving an indication of a point or region assumed to be located in the imaged pulp chamber of the image and corresponding to at least one image element of the image, the at least one image element being hereinafter referred to as "seed"; using at least the seed as initial segmentation mask; and iteratively carrying out a procedure, hereinafter referred to as "update procedure", comprising: determining candidate image elements for updating the segmentation mask, wherein determining comprises: in at least the first n iteration(s) of the update procedure, a grayscale thresholding taking as reference the current segmentation mask said grayscale thresholding comprising determining the differences between the intensity level of an image element under consideration and an averaged foreground and background intensity value, respectively, wherein the averaged foreground value is computed as the average intensity value of image elements comprised in the current segmentation mask and wherein the averaged background intensity value is computed as the average intensity value of image elements outside the current segmentation mask, wherein n is a nonzero positive integer; and, in at least one iteration of the update procedure, taking the curvature-based shape recognition into account; retaining, among the candidate image elements, a region of connected candidate image elements, the region comprising the seed; and using the retained region to update the segmentation mask. determining candidate image elements for updating the segmentation mask, wherein determining comprises: in at least the first n iteration(s) of the update procedure, a grayscale thresholding taking as reference the current segmentation mask said grayscale thresholding comprising determining the differences between the intensity level of an image element under consideration and an averaged foreground and background intensity value, respectively, wherein the averaged foreground value is computed as the average intensity value of image elements comprised in the current segmentation mask and wherein the averaged background intensity value is computed as the average intensity value of image elements outside the current segmentation mask, wherein n is a nonzero positive integer; and, in at least one iteration of the update procedure, taking the curvature-based shape recognition into account; in at least the first n iteration(s) of the update procedure, a grayscale thresholding taking as reference the current segmentation mask said grayscale thresholding comprising determining the differences between the intensity level of an image element under consideration and an averaged foreground and background intensity value, respectively, wherein the averaged foreground value is computed as the average intensity value of image elements comprised in the current segmentation mask and wherein the averaged background intensity value is computed as the average intensity value of image elements outside the current segmentation mask, wherein n is a nonzero positive integer; and, in at least one iteration of the update procedure, taking the curvature-based shape recognition into account; retaining, among the candidate image elements, a region of connected candidate image elements, the region comprising the seed; and using the retained region to update the segmentation mask.