title: Multi-atlas-based segmentation with local decision fusion--application to cardiac and aortic segmentation in CT scans.
abstract: A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely 1) single atlas-based segmentation, 2) average-shape atlas-based segmentation, and 3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance.
authors.affiliation: Department of Radiology, Image Sciences Institute,University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands. ivana@isi.uu.nl
authors.forename: Ivana
authors.initials: I
authors.lastname: Isgum
authors.forename: Marius
authors.initials: M
authors.lastname: Staring
authors.forename: Annemarieke
authors.initials: A
authors.lastname: Rutten
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000033:anatomy & histology*; D001027:Aortography; D006321:Heart / Q000033:anatomy & histology* / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D013902:Radiography, Thoracic; D014057:Tomography, X-Ray Computed / Q000379:methods*