ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Entangled decision forests and their application for semantic segmentation of CT images
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Learning image context for segmentation of prostate in CT-guided radiotherapy
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Image and Vision Computing
Beyond the line of sight: labeling the underlying surfaces
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Landmark detection in cardiac MRI using learned local image statistics
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Joint co-segmentation and registration of 3D ultrasound images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Hierarchical discriminative framework for detecting tubular structures in 3D images
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design in which the modeling and computing stages are studied in isolation. In this paper, we propose a learning algorithm, auto-context. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps created by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates until convergence. Auto-context integrates low-level and context information by fusing a large number of low-level appearance features with context and implicit shape information. The resulting discriminative algorithm is general and easy to implement. Under nearly the same parameter settings in training, we apply the algorithm to three challenging vision applications: foreground/background segregation, human body configuration estimation, and scene region labeling. Moreover, context also plays a very important role in medical/brain images where the anatomical structures are mostly constrained to relatively fixed positions. With only some slight changes resulting from using 3D instead of 2D features, the auto-context algorithm applied to brain MRI image segmentation is shown to outperform state-of-the-art algorithms specifically designed for this domain. Furthermore, the scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems.