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Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images.

Kreshuk A, Straehle CN, Sommer C, Koethe U, Cantoni M, Knott G, Hamprecht FA - PLoS ONE (2011)

Bottom Line: The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data.On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision).Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.

ABSTRACT
We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection.

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3D visualization of the results.Top: all synapses detected by the algorithm after training on the labels from Fig. 1. Bottom: a close-up view of three differently oriented synapses.
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pone-0024899-g003: 3D visualization of the results.Top: all synapses detected by the algorithm after training on the labels from Fig. 1. Bottom: a close-up view of three differently oriented synapses.

Mentions: A 3D view of the synapses detected by the algorithm based on the training set from Fig. 1 (with probability ratio threshold of 92%) is illustrated in Fig. 3.


Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images.

Kreshuk A, Straehle CN, Sommer C, Koethe U, Cantoni M, Knott G, Hamprecht FA - PLoS ONE (2011)

3D visualization of the results.Top: all synapses detected by the algorithm after training on the labels from Fig. 1. Bottom: a close-up view of three differently oriented synapses.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198725&req=5

pone-0024899-g003: 3D visualization of the results.Top: all synapses detected by the algorithm after training on the labels from Fig. 1. Bottom: a close-up view of three differently oriented synapses.
Mentions: A 3D view of the synapses detected by the algorithm based on the training set from Fig. 1 (with probability ratio threshold of 92%) is illustrated in Fig. 3.

Bottom Line: The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data.On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision).Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Center for Scientific Computing, University of Heidelberg, Heidelberg, Germany.

ABSTRACT
We describe a protocol for fully automated detection and segmentation of asymmetric, presumed excitatory, synapses in serial electron microscopy images of the adult mammalian cerebral cortex, taken with the focused ion beam, scanning electron microscope (FIB/SEM). The procedure is based on interactive machine learning and only requires a few labeled synapses for training. The statistical learning is performed on geometrical features of 3D neighborhoods of each voxel and can fully exploit the high z-resolution of the data. On a quantitative validation dataset of 111 synapses in 409 images of 1948×1342 pixels with manual annotations by three independent experts the error rate of the algorithm was found to be comparable to that of the experts (0.92 recall at 0.89 precision). Our software offers a convenient interface for labeling the training data and the possibility to visualize and proofread the results in 3D. The source code, the test dataset and the ground truth annotation are freely available on the website http://www.ilastik.org/synapse-detection.

Show MeSH