Paper Info
Title
Automated Segmentation Of Synapses In 3d Em Data
Abstract
This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive probability maps thresholding. On the test dataset, the algorithm missed considerably less synapses than the human expert during the ground truth creation, while maintaining an equivalent false positive rate. The algorithm is implemented as an extension to the Interactive Learning and Segmentation Toolkit "ilastik" and is freely available on our website (www.ilastik.org/synapse-detection).
Year
DOI
Venue
2011
10.1109/ISBI.2011.5872392
2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO
Keywords
Field
DocType
Synapse detection, neural tissue segmentation
Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Feature (computer vision),Segmentation,Image segmentation,Feature extraction,Artificial intelligence,Thresholding,Contextual image classification,Random forest
Conference
ISSN
Citations 
PageRank 
1945-7928
6
0.70
References 
Authors
2
6
Name
Order
Citations
PageRank
A. Kreshuk1395.58
Christoph N. Straehle21277.57
Christoph Sommer396260.51
Ullrich Koethe424922.37
Graham Knott51208.66
Fred A. Hamprecht696276.24