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

被引：109

作者：

Berger, Daniel R. ^{[1
]}

Seung, H. Sebastian ^{[2
]}

Lichtman, Jeff W. ^{[1
]}

机构：

[1] Harvard Univ, Dept Mol & Cellular Biol, Cambridge, MA 02138 USA

[2] Princeton Univ, Dept Comp Sci, Princeton Neurosci Inst, Princeton, NJ 08544 USA

来源：

FRONTIERS IN NEURAL CIRCUITS | 2018年 / 12卷

基金：

美国国家卫生研究院;

关键词：

connectomics; segmentation; visualization; serial section electron microscopy; CLEM; proofreading; TrakEM2; voxel; SCANNING-ELECTRON-MICROSCOPY; DIRECTION-SELECTIVITY; WIRING SPECIFICITY; HIGH-RESOLUTION; CIRCUIT; NETWORK; RECONSTRUCTION; ANATOMY; SYSTEM;

D O I：

10.3389/fncir.2018.00088

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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.

引用

页数：15

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