title: Automatic multi-resolution shape modeling of multi-organ structures.
abstract: Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases. In this paper, we introduce a new GEneralized Multi-resolution PDM (GEM-PDM) in the context of multi-organ analysis able to efficiently characterize the different inter-object relations, as well as the particular locality of each object separately. Importantly, unlike previous approaches, the configuration of the algorithm is automated thanks to a new agglomerative landmark clustering method proposed here, which equally allows us to identify smaller anatomically significant regions within organs. The significant advantage of the GEM-PDM method over two previous approaches (PDM and hierarchical PDM) in terms of shape modeling accuracy and robustness to noise, has been successfully verified for two different databases of sets of multiple organs: six subcortical brain structures, and seven abdominal organs. Finally, we propose the integration of the new shape modeling framework into an active shape-model-based segmentation algorithm. The resulting algorithm, named GEMA, provides a better overall performance than the two classical approaches tested, ASM, and hierarchical ASM, when applied to the segmentation of 3D brain MRI.
authors.affiliation: Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Health System, Washington DC 20009, USA. Electronic address: jcerrola@cnmc.org.
authors.forename: Juan J
authors.initials: JJ
authors.lastname: Cerrolaza
authors.affiliation: Surgical Technology and Biomechanics Department, University of Bern, Bern, Switzerland. Electronic address: mauricio.reyes@istb.unibe.ch.
authors.forename: Mauricio
authors.initials: M
authors.lastname: Reyes
authors.affiliation: Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD 20814, USA. Electronic address: rms@nih.gov.
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
authors.affiliation: ICREA, and Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: ma.gonzalez@upf.edu.
authors.forename: Miguel Ángel
authors.initials: MÁ
authors.lastname: González-Ballester
authors.affiliation: Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Health System, Washington DC 20009, USA; School of Medicine and Health Sciences, George Washington University, Washington DC, USA. Electronic address: mlingura@cnmc.org.
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
mesh_terms: D000005:Abdomen / Q000033:anatomy & histology*; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D011860:Radiography, Abdominal / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Active shape model; Hierarchical modeling; Multi-resolution; Point distribution model (PDM); Statistical shape model