DP2: Distributed 3D image segmentation using micro-labor workforce

Summary: This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon’s Mechanical Turk system provides the labor in a scalable manner. Availability and implementation: Python-based code for non-commercial use and test data are available in the source archive at https://sites.google.com/site/imagecrowdseg/. Contact: rgiuly@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

[1]  S. B. Leighton SEM images of block faces, cut by a miniature microtome within the SEM - a technical note. , 1981, Scanning electron microscopy.

[2]  Leighton Sb SEM images of block faces, cut by a miniature microtome within the SEM - a technical note. , 1981 .

[3]  Michael J Ackerman,et al.  Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit. , 2002, Studies in health technology and informatics.

[4]  W. Denk,et al.  Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure , 2004, PLoS biology.

[5]  Joseph F. Murray,et al.  Supervised Learning of Image Restoration with Convolutional Networks , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[7]  Markus Hadwiger,et al.  Scalable and Interactive Segmentation and Visualization of Neural Processes in EM Datasets , 2009, IEEE Transactions on Visualization and Computer Graphics.

[8]  M. Eisenstein,et al.  Neural circuits: Putting neurons on the map , 2009, Nature.

[9]  Yuriy Mishchenko,et al.  Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs , 2009, Journal of Neuroscience Methods.

[10]  Joachim M. Buhmann,et al.  Neuron geometry extraction by perceptual grouping in ssTEM images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Ross T. Whitaker,et al.  Detection of neuron membranes in electron microscopy images using a serial neural network architecture , 2010, Medical Image Anal..

[12]  R. Tsien,et al.  Enhancing Serial Block-Face Scanning Electron Microscopy to Enable High Resolution 3-D Nanohistology of Cells and Tissues , 2010 .

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

[14]  Horst Bischof,et al.  Neural Process Reconstruction from Sparse User Scribbles , 2011, MICCAI.