title: Multi-organ localization with cascaded global-to-local regression and shape prior.
abstract: We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize the global-to-local cascade of regression random forest to multiple organs. A first regressor encodes the global relationships between organs, learning simultaneously all organs parameters. Then subsequent regressors refine the localization of each organ locally and independently for improved accuracy. By combining the regression vote distribution and the organ shape prior (through probabilistic atlas representation) we compute confidence maps that are organ-dedicated probability maps. They are used within the cascade itself, to better select the test voxels for the second set of regressors, and to provide richer information than the classical bounding boxes result thanks to the shape prior. We propose an extensive study of the different learning and testing parameters, showing both their robustness to reasonable perturbations and their influence on the final algorithm accuracy. Finally we demonstrate the robustness and accuracy of our approach by evaluating the localization of six abdominal organs (liver, two kidneys, spleen, gallbladder and stomach) on a large and diverse database of 130 CT volumes. Moreover, the comparison of our results with two existing methods shows significant improvements brought by our approach and our deep understanding and optimization of the parameters.
authors.affiliation: Philips Research MediSys, 33 rue de Verdun, Suresnes Cedex 92156, France; Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 46 Rue Barrault, Paris 75013, France. Electronic address: romanegauriau@gmail.com.
authors.forename: Romane
authors.initials: R
authors.lastname: Gauriau
authors.affiliation: Philips Research MediSys, 33 rue de Verdun, Suresnes Cedex 92156, France.
authors.forename: Rémi
authors.initials: R
authors.lastname: Cuingnet
authors.affiliation: Philips Research MediSys, 33 rue de Verdun, Suresnes Cedex 92156, France.
authors.forename: David
authors.initials: D
authors.lastname: Lesage
authors.affiliation: Institut Mines-Telecom, Telecom ParisTech, CNRS LTCI, 46 Rue Barrault, Paris 75013, France.
authors.forename: Isabelle
authors.initials: I
authors.lastname: Bloch
mesh_terms: D002765:Cholecystography; D003663:Decision Trees; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D007668:Kidney / Q000000981:diagnostic imaging; D008099:Liver / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D013154:Spleen / Q000000981:diagnostic imaging; D013270:Stomach / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: 3D CT; Abdominal organs; Multi-organ localization; Random forest; Regression