OBJECTIVE: A fully automated and efficient method for segmenting ten major structures within the heart in Cardiac CT Angiography data for the purposes of display or cardiac functional analysis. MATERIALS AND METHODS: A spatially varying Gaussian classifier is a flexible model for segmentation, combining the advantages of atlas-based frameworks, with supervised intensity models. It is composed of an independent Gaussian classifier at each voxel and uses non-rigid registration for the initial spatial alignment. We show how this large model can be trained efficiently and present a novel smoothing technique based on normalised convolution to mitigate inherent overfitting issues. The 30 datasets used in this study are selected from a variety of different scanners in order to test the robustness and stability of the algorithm. The datasets were manually segmented by a trained clinician. RESULTS: The method was evaluated in a leave-one-out fashion, and the results were compared to other state of the art methods in the field, with a mean surface-to-surface distance of between 0.61 and 2.12 mm for different compartments. CONCLUSION: The accuracy of this method is comparable to other state of the art methods in the field. Its benefits lie in its conceptual simplicity and its general applicability. Only one non-rigid registration is required, giving it a speed advantage over multi-atlas approaches. Further accuracy may be achievable through the incorporation of an explicit shape model.
OBJECTIVE: A fully automated and efficient method for segmenting ten major structures within the heart in Cardiac CT Angiography data for the purposes of display or cardiac functional analysis. MATERIALS AND METHODS: A spatially varying Gaussian classifier is a flexible model for segmentation, combining the advantages of atlas-based frameworks, with supervised intensity models. It is composed of an independent Gaussian classifier at each voxel and uses non-rigid registration for the initial spatial alignment. We show how this large model can be trained efficiently and present a novel smoothing technique based on normalised convolution to mitigate inherent overfitting issues. The 30 datasets used in this study are selected from a variety of different scanners in order to test the robustness and stability of the algorithm. The datasets were manually segmented by a trained clinician. RESULTS: The method was evaluated in a leave-one-out fashion, and the results were compared to other state of the art methods in the field, with a mean surface-to-surface distance of between 0.61 and 2.12 mm for different compartments. CONCLUSION: The accuracy of this method is comparable to other state of the art methods in the field. Its benefits lie in its conceptual simplicity and its general applicability. Only one non-rigid registration is required, giving it a speed advantage over multi-atlas approaches. Further accuracy may be achievable through the incorporation of an explicit shape model.
Authors: Bruce Fischl; David H Salat; Evelina Busa; Marilyn Albert; Megan Dieterich; Christian Haselgrove; Andre van der Kouwe; Ron Killiany; David Kennedy; Shuna Klaveness; Albert Montillo; Nikos Makris; Bruce Rosen; Anders M Dale Journal: Neuron Date: 2002-01-31 Impact factor: 17.173
Authors: S C Mitchell; B P Lelieveldt; R J van der Geest; H G Bosch; J H Reiber; M Sonka Journal: IEEE Trans Med Imaging Date: 2001-05 Impact factor: 10.048
Authors: Maria Lorenzo-Valdés; Gerardo I Sanchez-Ortiz; Andrew G Elkington; Raad H Mohiaddin; Daniel Rueckert Journal: Med Image Anal Date: 2004-09 Impact factor: 8.545
Authors: Olivier Ecabert; Jochen Peters; Hauke Schramm; Cristian Lorenz; Jens von Berg; Matthew J Walker; Mani Vembar; Mark E Olszewski; Krishna Subramanyan; Guy Lavi; Jürgen Weese Journal: IEEE Trans Med Imaging Date: 2008-09 Impact factor: 10.048
Authors: Olivier Ecabert; Jochen Peters; Matthew J Walker; Thomas Ivanc; Cristian Lorenz; Jens von Berg; Jonathan Lessick; Mani Vembar; Jürgen Weese Journal: Med Image Anal Date: 2011-06-16 Impact factor: 8.545
Authors: H A Kirişli; M Schaap; S Klein; S L Papadopoulou; M Bonardi; C H Chen; A C Weustink; N R Mollet; E J Vonken; R J van der Geest; T van Walsum; W J Niessen Journal: Med Phys Date: 2010-12 Impact factor: 4.071