Pictorial Structures for Object Recognition
International Journal of Computer Vision
Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Spine detection and labeling using a parts-based graphical model
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Automatic learning sparse correspondences for initialising groupwise registration
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
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We propose a method for accurately localizing anatomical landmarks in 3D medical volumes based on dense matching of parts-based graphical models. Our novel approach replaces population mean models by jointly leveraging weighted combinations of labeled exemplars (both spatial and appearance) to obtain personalized models for the localization of arbitrary landmarks in upper body images. We compare the method to a baseline population-mean graphical model and atlas-based deformable registration optimized for CT-CT registration, by measuring the localization accuracy of 22 anatomical landmarks in clinical 3D CT volumes, using a database of 83 lung cancer patients. The average mean localization error across all landmarks is 2.35 voxels. Our proposed method outperforms deformable registration by 73%, 93% for the most improved landmark. Compared to the baseline population-mean graphical model, the average improvement of localization accuracy is 32%; 67% for the most improved landmark.