3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries

• Verfasst von: Andres, Björn [VerfasserIn]
• Verfasst von: Köthe, Ullrich [VerfasserIn]
• Verfasst von: Kröger, Thorben [VerfasserIn]
• Verfasst von: Denk, Winfried [VerfasserIn]
• Verfasst von: Hamprecht, Fred [VerfasserIn]
• Titel: 3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries
• Verf.angabe: Bjoern Andres, Ullrich Koethe, Thorben Kroeger, Moritz Helmstaedter, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht
• Jahr: 2012
• Umfang: 10 S.
• Fussnoten: Available online 19 December 2011 ; Gesehen am 06.06.2018
• Titel Quelle: Enthalten in: Medical image analysis
• Ort Quelle: Amsterdam [u.a.] : Elsevier Science, 1996
• Jahr Quelle: 2012
• Band/Heft Quelle: 16(2012), 4, Seite 796-805
• ISSN Quelle: 1361-8423
• DOI: doi:10.1016/j.media.2011.11.004
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Algorithms
• Sach-SW: Animals
• Sach-SW: Computer Graphics
• Sach-SW: Computer Simulation
• Sach-SW: Humans
• Sach-SW: Image Enhancement
• Sach-SW: Image Interpretation, Computer-Assisted
• Sach-SW: Imaging, Three-Dimensional
• Sach-SW: Microscopy
• Sach-SW: Models, Anatomic
• Sach-SW: Neuropil
• Sach-SW: Pattern Recognition, Automated
• Sach-SW: Rabbits
• Sach-SW: Reproducibility of Results
• Sach-SW: Sensitivity and Specificity
• Sach-SW: Subtraction Technique
• K10plus-PPN: 1576047024

Abstract

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 7.5billionvoxels from the inner plexiform layer of rabbit retina. We provide C++ source code for annotation, geometry extraction, training and inference.