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          Institute: MPI für medizinische Forschung     Collection: jahrbuch_2011_archival     Display Documents

ID: 597748.0, MPI für medizinische Forschung / jahrbuch_2011_archival
3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries
Translation of Title:3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries
Authors:Andres, Bjoern; Koethe, Ullrich; Kroeger, Thorben; Helmstaedter, Moritz; Briggman, Kevin L.; Denk, Winfried; Hamprecht, Fred A.
Date of Publication (YYYY-MM-DD):2011-12-19
Title of Journal:Medical Image Analysis
Journal Abbrev.:Medical Image Analysis
Review Status:Peer-review
Audience:Experts Only
Intended Educational Use:No
Abstract / Description:The segmentation of large volume images of neuropil acquired by serial sectioning electron microscopy is an important step toward the 3D reconstruction of neural circuits. The only cue provided by the data at hand is boundaries between otherwise indistinguishable objects. This indistinguishability, combined with the boundaries becoming very thin or faint in places, makes the large body of work on region−based segmentation methods inapplicable. On the other hand, boundary−based methods that exploit purely local evidence do not reach the extremely high accuracy required by the application domain that cannot tolerate the global topological errors arising from false local decisions. As a consequence, we propose a supervoxel merging method that arrives at its decisions in a non−local fashion, by posing and approximately solving a joint combinatorial optimization problem over all faces between supervoxels. The use of supervoxels allows the extraction of expressive geometric features. These are used by the higher−order potentials in a graphical model that assimilate knowledge about the geometry of neural surfaces by automated training on a gold standard. The scope of this improvement is demonstrated on the benchmark dataset E1088 (Helmstaedter et al., 2011) of 7.5 billion voxels from the inner plexiform layer of rabbit retina. We provide C++ source code for annotation, geometry extraction, training and inference
Free Keywords:Segmentation;
Circuit reconstruction;
Graphical model;
Random forest
External Publication Status:published
Document Type:Article
Communicated by:Wulf Kaiser
Affiliations:MPI für medizinische Forschung/Abteilung Zellphysiologie
MPI für medizinische Forschung/Abteilung Biomedizinische Optik
MPI für medizinische Forschung/Abteilung Zellphysiologie/Kortikale Schaltkreise
MPI für medizinische Forschung/Abteilung Biomedizinische Optik/Gruppe Moritz Helmstaedter
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