Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
Model-based Evaluation of Image Segmentation Methods
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
Error Metrics for Quantitative Evaluation of Medical Image Segmentation
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
Medical Image Synthesis via Monte Carlo Simulation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Evaluation of Image Quality in Medical Volume Visualization: The State of the Art
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Comparison and Evaluation of Segmentation Techniques for Subcortical Structures in Brain MRI
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Simulation of Ground-Truth Validation Data Via Physically- and Statistically-Based Warps
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Assessment of Reliability of Multi-site Neuroimaging Via Traveling Phantom Study
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Estimation of Inferential Uncertainty in Assessing Expert Segmentation Performance from Staple
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Level-set segmentation of brain tumors using a threshold-based speed function
Image and Vision Computing
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Primal/dual linear programming and statistical atlases for cartilage segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Revisiting the evaluation of segmentation results: introducing confidence maps
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
4D ventricular segmentation and wall motion estimation using efficient discrete optimization
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
A segmentation framework for abdominal organs from CT scans
Artificial Intelligence in Medicine
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
3D shape context surface registration for cortical mapping
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Automatic segmentation and components classification of optic pathway gliomas in MRI
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Inferring the performance of medical imaging algorithms
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Segmentation of skull base tumors from MRI using a hybrid support vector machine-based method
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Atlas guided identification of brain structures by combining 3d segmentation and SVM classification
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Vision-based user interfaces for health applications: a survey
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Introduction to the non-rigid image registration evaluation project (NIREP)
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
A graph-based technique for semi-supervised segmentation of 3D surfaces
Pattern Recognition Letters
Prediction of brain MR scans in longitudinal tumor follow-up studies
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Performance divergence with data discrepancy: a review
Artificial Intelligence Review
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Extracting 3D structures from volumetric images like MRI or CT is becoming a routine process for diagnosis based on quantitation, for radiotherapy planning, for surgical planning and image-guided intervention, for studying neurodevelopmental and neurodegenerative aspects of brain diseases, and for clinical drug trials. Key issues for segmenting anatomical objects from 3D medical images are validity and reliability. We have developed VALMET, a new tool for validation and comparison of object segmentation. New features not available in commercial and public-domain image processing packages are the choice between different metrics to describe differences between segmentations and the use of graphical overlay and 3D display for visual assessment of the locality and magnitude of segmentation variability. Input to the tool are an original 3D image (MRI, CT, ultrasound), and a series of segmentations either generated by several human raters and/or by automatic methods (machine). Quantitative evaluation includes intra-class correlation of resulting volumes and four different shape distance metrics, a) percentage overlap of segmented structures (R intersect S)/(R union S), b) probabilistic overlap measure for non-binary segmentations, c) mean/median absolute distances between object surfaces, and maximum (Hausdorff) distance. All these measures are calculated for arbitrarily selected 2D cross-sections and full 3D segmentations. Segmentation results are overlaid onto the original image data for visual comparison. A 3D graphical display of the segmented organ is color-coded depending on the selected metric for measuring segmentation difference. The new tool is in routine use for intra- and inter-rater reliability studies and for testing novel automatic machine-segmentation versus a gold standard established by human experts. Preliminary studies showed that the new tool could significantly improve intra- and inter-rater reliability of hippocampus segmentation to achieve intra-class correlation coefficients significantly higher than published elsewhere.