Active shape models—their training and application
Computer Vision and Image Understanding
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Regression forests for efficient anatomy detection and localization in CT studies
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Automatic multi-organ segmentation using learning-based segmentation and level set optimization
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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We propose a novel framework for rapid and accurate segmentation of a cohort of organs. First, it integrates local and global image context through a product rule to simultaneously detect multiple landmarks on the target organs. The global posterior integrates evidence over all volume patches, while the local image context is modeled with a local discriminative classifier. Through non-parametric modeling of the global posterior, it exploits sparsity in the global context for efficient detection. The complete surface of the target organs is then inferred by robust alignment of a shape model to the resulting landmarks and finally deformed using discriminative boundary detectors. Using our approach, we demonstrate efficient detection and accurate segmentation of liver, kidneys, heart, and lungs in challenging low-resolution MR data in less than one second, and of prostate, bladder, rectum, and femoral heads in CT scans, in roughly one to three seconds and in both cases with accuracy fairly close to inter-user variability.