Mitochondria Detection in EM Stacks


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Electron microscopy (EM) is key to mapping the morphology of neural structures. Recent techniques, such as Focused Ion Beam Scanning Electron Microscopy (FIB-SEM), can now deliver image stacks at the nanometer resolution in all three dimensions. Such stacks show very fine structures that are critical to unlocking new insights into brain function but are still mostly analyzed by hand, which can require months of tedious labor. As a result, the vast majority of this very high quality data goes unused. Furthermore, although they contain tens of millions of voxels, these stacks span volumes smaller than 10x10x10 μm3, which presents less than a billionth of the volume of the entire brain. If it is ever to be mapped in its entirety, automation will be required. Our goal is to propose a fully automated approach that can segment large EM datasets by using sophisticated cues that capture global shape and texture.

A. Lucchi, K. Smith, R. Achanta, V. Lepetit, G. Knott, P. Fua

Results

Automatic segmentation of mitochondria also called “cellular power plants” from a FIB-SEM image stack. The blue overlay indicates areas segmented as mitochondria by grouping individual voxels into supervoxels, computing shape and intensity features from these supervoxels, and feeding them to a graph-cut algorithm.

The voxels that were painted blue in the above example are carved out of the volume to produce a 3D reconstruction of the mitochondria. Note how elongated some of them are.

Materials

References

A. Lucchi; P. Márquez-Neila; C. Becker; Y. Li; K. Smith et al. : Learning Structured Models for Segmentation of 2D and 3D Imagery; IEEE Transactions on Medical Imaging. 2015. DOI : 10.1109/TMI.2014.2376274.
A. Lucchi; C. J. Becker; P. Marquez Neila; P. Fua : Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation. 2014. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, Massachusetts, USA, September 2014.
A. Lucchi; Y. Li; P. Fua : Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets. 2013. Conference on Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, USA, June 23-28, 2013. DOI : 10.1109/Cvpr.2013.259.
A. Lucchi; Y. Li; K. Smith; P. Fua : Structured Image Segmentation using Kernelized Features. 2012. European Conference on Computer Vision, Florence, Italy, October 2012.
A. Lucchi; K. Smith; R. Achanta; G. Knott; P. Fua : Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks with Learned Shape Features; IEEE Transactions on Medical Imaging. 2011. DOI : 10.1109/TMI.2011.2171705.