High resolution segmentation of neuronal tissues from low depth-resolution EM imagery.

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8th International Conference of Energy Minimization Methods in Computer Vision and Pattern Recognition Energy Minimization Methods in Computer Vision and Pattern Recognition. 2011;6819:261-72

High resolution segmentation of neuronal tissues from low depth-resolution EM imagery. Scheffer LabHess LabFetter Lab

Glasner D, Hu T, Nunez-Iglesias J, Scheffer L, Xu C, Hess H, Fetter R, Chklovskii D, Basri R

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Abstract

The challenge of recovering the topology of massive neuronal circuits can potentially be met by high throughput Electron Microscopy (EM) imagery. Segmenting a 3-dimensional stack of EM images into the individual neurons is difficult, due to the low depth-resolution in existing high-throughput EM technology, such as serial section Transmission EM (ssTEM). In this paper we propose methods for detecting the high resolution locations of membranes from low depth-resolution images. We approach this problem using both a method that learns a discriminative, over-complete dictionary and a kernel SVM. We test this approach on tomographic sections produced in simulations from high resolution Focused Ion Beam (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.

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High resolution segmentation of neuronal tissues from low depth-resolution EM imagery.

Abstract