Semiautomated thoracic and abdominal computed tomography segmentation using the belief functions theory : Application to 3D internal dosimetry

Aim: Segmentation of computed tomography (CT) images is an important step in three-dimensional (3D) internal dosimetry. To this end, a semiautomated method was developed to delineate organs, using the belief functions theory. Materials and Methods: The membership degree of each voxel to each volume of interest is estimated by computing a basic belief assignment (bba). For each voxel V, bbas corresponding to each neighbor are aggregated to obtain a unique bba, using a merging procedure. Before aggregating information, a 3D filter is applied, in order to take into account the fact that the more the voxel Vi is close to V, the more the information coming from Vi is reliable. The aim is to weaken the contribution of voxels according to their distance with respect to the voxel to be classified. The algorithm was applied on 10 CT scans (pixel size, 0.98 × 0.98 mm2, slice thickness 3 mm, 120 kV). For each organ (i.e., the lung, liver, kidney, and spleen), the algorithm was applied on a part of the CT volume. Fir...