Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watersnakes: Energy-Driven Watershed Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The GeoMap: a unified representation for topology and geometry
GbRPR'05 Proceedings of the 5th IAPR international conference on Graph-Based Representations in Pattern Recognition
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Multiscale gradient watersheds of color images
IEEE Transactions on Image Processing
Classification-Driven Watershed Segmentation
IEEE Transactions on Image Processing
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
A fully automated approach to segmentation of irregularly shaped cellular structures in EM images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Spatial decision forests for MS lesion segmentation in multi-channel MR images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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
Neural process reconstruction from sparse user scribbles
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Carving: scalable interactive segmentation of neural volume electron microscopy images
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
An interactive editing framework for electron microscopy image segmentation
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
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Three-dimensional electron-microscopic image stacks with almost isotropic resolution allow, for the first time, to determine the complete connection matrix of parts of the brain. In spite of major advances in staining, correct segmentation of these stacks remains challenging, because very few local mistakes can lead to severe global errors. We propose a hierarchical segmentation procedure based on statistical learning and topology-preserving grouping. Edge probability maps are computed by a random forest classifier (trained on hand-labeled data) and partitioned into supervoxels by the watershed transform. Over-segmentation is then resolved by another random forest. Careful validation shows that the results of our algorithm are close to human labelings.