Database-Guided Segmentation of Anatomical Structures with Complex Appearance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements
Journal of Mathematical Imaging and 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
Fast and Robust 3-D MRI Brain Structure Segmentation
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Spine detection and labeling using a parts-based graphical model
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Automated planning of scan geometries in spine MRI scans
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Robust MR spine detection using hierarchical learning and local articulated model
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Fast anatomical structure localization using top-down image patch regression
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Automated identification of thoracolumbar vertebrae using orthogonal matching pursuit
Machine Vision and Applications
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Determining spinal geometry and in particular the position and orientation of the intervertebral disks is an integral part of nearly every spinal examination with Computed Tomography (CT) and Magnetic Resonance (MR) imaging. It is particularly important for the standardized alignment of the scan geometry with the spine. In this paper, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the intervertebral disks in a given spinal image volume. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Since the proposed approach is learning-based it can be trained for MR or CT alike. Experimental results based on 42 MR volumes show that our system not only achieves superior accuracy but also is the fastest system of its kind in the literature - on average, the spinal disks of a whole spine are detected in 11.5s with 98.6% sensitivity and 0.073 false positive detections per volume. An average position error of 2.4mm and angular error of 3.9° is achieved.