title: Using Frankenstein's creature paradigm to build a patient specific atlas.
abstract: Conformal radiotherapy planning needs accurate delineations of the critical structures. Atlas-based segmentation has been shown to be very efficient to delineate brain structures. It would therefore be very interesting to develop an atlas for the head and neck region where 7% of the cancers arise. However, the construction of an atlas in this region is very difficult due to the high variability of the anatomies. This can generate segmentation errors and over-segmented structures in the atlas. To overcome this drawback, we present an alternative method to build a template locally adapted to the patient's anatomy. This is done first by selecting in a database the images that are the most similar to the patient on predefined regions of interest, using on a distance between transformations. The first major contribution is that we do not compute every patient-to-image registration to find the most similar image, but only the registration of the patient towards an average image. This method is therefore computationally very efficient. The second major contribution is a novel method to use the selected images and the predefined regions to build a "Frankenstein's creature" for segmentation. We present a qualitative and quantitative comparison between the proposed method and a classical atlas-based segmentation method. This evaluation is performed on a subset of 58 patients among a database of 105 head and neck CT images and shows a great improvement of the specificity of the results.
authors.affiliation: CRL, Children's Hospital Boston, Harvard Medical School, USA. Olivier.Commowick@childrens.harvard.edu
authors.forename: Olivier
authors.initials: O
authors.lastname: Commowick
authors.forename: Simon K
authors.initials: SK
authors.lastname: Warfield
authors.forename: Grégoire
authors.initials: G
authors.lastname: Malandain
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*