High Resolution Segmentation of Neuronal Tissues from Low Depth-Resolution EM Imagery

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. Key words: Segmentation of neuronal tissues, Task-driven dictionary learning, Sparse over-complete representation, Connectomics

• Type: Journal
• Year: 2011
• Where: EMMCVPR
• Authors: Daniel Glasner, Tao Hu, Juan Nunez-Iglesias, Lou Scheffer, Shan Xu, Harald F. Hess, Richard Fetter, Dmitri B. Chklovskii, Ronen Basri
• Keywords: Computer Vision, Depth Resolution, Electron Microscopy, EMMCVPR 2011, Neuronal Circuits