A Context-aware Delayed Agglomeration Framework for EM Segmentation

This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.

[1]  Eric L. Miller,et al.  Segmentation fusion for connectomics , 2011, 2011 International Conference on Computer Vision.

[2]  Toufiq Parag,et al.  Annotating Synapses in Large EM Datasets , 2014, ArXiv.

[3]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[4]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Louis K. Scheffer,et al.  Semi-automated reconstruction of neural circuits using electron microscopy , 2010, Current Opinion in Neurobiology.

[6]  Louis K. Scheffer,et al.  A visual motion detection circuit suggested by Drosophila connectomics , 2013, Nature.

[7]  Ullrich Köthe,et al.  Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification , 2008, DAGM-Symposium.

[8]  Vladlen Koltun,et al.  Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.

[9]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[10]  Ullrich Köthe,et al.  Globally Optimal Closed-Surface Segmentation for Connectomics , 2012, ECCV.

[11]  Pascal Fua,et al.  Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features , 2012, IEEE Transactions on Medical Imaging.

[12]  H. Sebastian Seung,et al.  Learning to Agglomerate Superpixel Hierarchies , 2011, NIPS.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[14]  Ullrich Köthe,et al.  Ilastik: Interactive learning and segmentation toolkit , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[15]  H. Sebastian Seung,et al.  Boundary Learning by Optimization with Topological Constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Jianbo Shi,et al.  Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images , 2013, PloS one.

[17]  Pushmeet Kohli,et al.  Associative hierarchical CRFs for object class image segmentation , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[18]  Matthew Cook,et al.  Efficient automatic 3D-reconstruction of branching neurons from EM data , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Louis K. Scheffer,et al.  Small Sample Learning of Superpixel Classifiers for EM Segmentation , 2014, MICCAI.

[20]  Marina Meila,et al.  Comparing Clusterings by the Variation of Information , 2003, COLT.

[21]  M. Helmstaedter.  Cellular-resolution connectomics: challenges of dense neural circuit reconstruction , 2013, Nature Methods.