title: Multi-organ localization combining global-to-local regression and confidence maps.
abstract: We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize global-to-local cascades of regression forests [1] to multiple organs. A first regressor encodes global relationships between organs. Subsequent regressors refine the localization of each organ locally and independently for improved accuracy. We introduce confidence maps, which incorporate information about both the regression vote distribution and the organ shape through probabilistic atlases. 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 thanks to the shape prior. We demonstrate the robustness and accuracy of our approach through a quantitative evaluation on a large database of 130 CT volumes.
authors.forename: Romane
authors.initials: R
authors.lastname: Gauriau
authors.forename: Rémi
authors.initials: R
authors.lastname: Cuingnet
authors.forename: David
authors.initials: D
authors.lastname: Lesage
authors.forename: Isabelle
authors.initials: I
authors.lastname: Bloch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016001:Confidence Intervals; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011860:Radiography, Abdominal / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014781:Viscera / Q000000981:diagnostic imaging*