Active shape models—their training and application
Computer Vision and Image Understanding
A Framework for Uncertainty and Validation of 3-D RegistrationMethods Based on Points and Frames
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Image Segmentation Algorithms Using the Pareto Front
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Valmet: A New Validation Tool for Assessing and Improving 3D Object Segmentation
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Understanding Intensity Non-uniformity in MRI
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Meta-Evaluation of Image Segmentation Using Machine Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Adaptative evaluation of image segmentation results
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A Ground Truth Correspondence Measure for Benchmarking
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Introduction to the non-rigid image registration evaluation project (NIREP)
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Reliability-driven, spatially-adaptive regularization for deformable registration
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
Probabilistic multi-shape segmentation of knee extensor and flexor muscles
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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The problem of scarcity of ground-truth expert delineations of medical image data is a serious one that impedes the training and validation of medical image analysis techniques. We develop an algorithm for the automatic generation of large databases of annotated images from a single reference dataset. We provide a web-based interface through which the users can upload a reference data set (an image and its corresponding segmentation and landmark points), provide custom setting of parameters, and, following server-side computations, generate and download an arbitrary number of novel ground-truth data, including segmentations, displacement vector fields, intensity non-uniformity maps, and point correspondences. To produce realistic simulated data, we use variational (statistically-based) and vibrational (physically-based) spatial deformations, nonlinear radiometric warps mimicking imaging non-homogeneity, and additive random noise with different underlying distributions. We outline the algorithmic details, present sample results, and provide the web address to readers for immediate evaluation and usage.