Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections

Abstract

Abstract. Connectomics based on high resolution ssTEM imagery requires reconstruction of the neuron geometry from histological slides. We present an approach for the automatic membrane segmentation in anisotropic stacks of electron microscopy brain tissue sections. The ambiguities in neuronal segmentation of a section are resolved by using the context from the neighboring sections. We find the global dense correspondence between the sections by SIFT Flow algorithm, evaluate the features of the corresponding pixels and use them to perform the segmentation. Our method is 3.6 and 6.4% more accurate in two different accuracy metrics than the algorithm with no context from other sections.

SuperSlicing Frame Restoration for Anisotropic ssTEM and Video Data

“…Warping error One-section segmentation Kaynig et al (2010) 2.876 * 10 −3 Three consecutive sections Laptev et al (2012) 2.693 * 10 −3 SuperSlicing segmentation 2.384 * 10 −3 Table 1: Warping error on a testing set for one-section segmentation, segmentation based on three consecutive sections and for SuperSlicing. Our method outperforms the baseline methods by 17% and 11%, respectively.…”

“…To quantitatively test the approach for neuronal membrane segmentation presented in section 4, we compare segmentation results with two more methods: RF segmentation based on only features evaluated in one layer Kaynig et al (2010), and RF segmentation based on context from neighboring sections Laptev et al (2012). For fair comparison we implement the same set of features for all three methods and use the same RF structure with no post-processing to measure the impact of SuperSlicing.…”

“…The first approach Kaynig et al (2010) focuses on the detection of neuron membranes in each section independently based only on local information around every pixel. The second approach Laptev et al (2012) incorporates context from different sections to resolve ambiguities that cannot be resolved within one section. The biggest challenge for the segmentation algorithm is posed by the blurry membranes (see Fig.5), that are often the result of anisotropy.…”

Refining Mitochondria Segmentation in Electron Microscopy Imagery with Active Surfaces

“…These statistics require not just object detection, but also very accurate surface structure delineation. State-of-the-art image segmentation algorithms such as [6,1,7,10] produce reasonably good localization results, in that they are able to detect most instances of the object they are searching for, and provide a rough outline. However, they often fail to accurately define the detailed boundary surface of the object in question to the precision required for accurate geometric measurements, especially surface area.…”

Flash Scanning Electron Microscopy

“…In this paper, we therefore address the problem of speeding up SEM imaging for the purpose of detecting and measuring specific intra-cellular structures. While the majority of related research has focused on providing automatic segmentation and labeling tools [5,6,7], very few have addressed this throughput …”