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Modeling the Coronary Artery Tree., , , , and . SMI, page 354-357. IEEE Computer Society, (2004)Temporal subtraction of chest radiographs compensating pose differences., , , , , , , and . Medical Imaging: Image Processing, volume 7962 of SPIE Proceedings, page 79620U. SPIE, (2011)Deep Learning Based Rib Centerline Extraction and Labeling., , , , , and . MSKI@MICCAI, volume 11404 of Lecture Notes in Computer Science, page 99-113. Springer, (2018)Detection and Localization of Landmarks in the Lower Extremities Using an Automatically Learned Conditional Random Field., , , , , , and . GRAIL/MFCA/MICGen@MICCAI, volume 10551 of Lecture Notes in Computer Science, page 64-75. Springer, (2017)Abstract: Does Bone Suppression and Lung Detection Improve Chest Disease Classification?, , , , , , , , and . Bildverarbeitung für die Medizin, page 184. Springer Vieweg, (2019)Coupled deformable models with spatially varying features for quantitative assessment of left ventricular function from cardiac MRI., , , , , , , , and . Medical Imaging: Image Processing, volume 5032 of SPIE Proceedings, SPIE, (2003)A grammar-based speech user interface generator for structured reporting.. CARS, volume 1256 of International Congress Series, page 887-892. Elsevier, (2003)When does Bone Suppression and Lung Field Segmentation Improve Chest X-Ray Disease Classification?, , , , , , , , and . CoRR, (2018)Detection and localization of spatially correlated point landmarks in medical images using an automatically learned conditional random field., , , , , , and . Computer Vision and Image Understanding, (2018)3D reconstruction of the human rib cage from 2D projection images using a statistical shape model., , , , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 5 (2): 111-124 (2010)