4.3 Article

VAST (Volume Annotation and Segmentation Tool): Efficient Manual and Semi-Automatic Labeling of Large 3D Image Stacks

期刊

FRONTIERS IN NEURAL CIRCUITS
卷 12, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fncir.2018.00088

关键词

connectomics; segmentation; visualization; serial section electron microscopy; CLEM; proofreading; TrakEM2; voxel

资金

  1. Gatsby Charitable Foundation
  2. Howard Hughes Medical Institute
  3. Human Frontier Science Program
  4. National Institutes of Health
  5. NIH/NINDS [U19 NS104653-01]
  6. IARPA/Department of the Interior (Algorithms for representation and inference informed by the acquisition of data from neuroscience experiments, ARIADNE) [D16PC00002]
  7. NIH/NIMH (Conte Center Award) [P50MH094271]
  8. Department of Defense (Columbia University) [GG008784]
  9. Broad Institute [6600029-5500000959]

向作者/读者索取更多资源

Recent developments in serial-section electron microscopy allow the efficient generation of very large image data sets but analyzing such data poses challenges for software tools. Here we introduce Volume Annotation and Segmentation Tool (VAST), a freely available utility program for generating and editing annotations and segmentations of large volumetric image (voxel) data sets. It provides a simple yet powerful user interface for real-time exploration and analysis of large data sets even in the Petabyte range.

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