Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections
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- Laptev D., Vezhnevets A., Dwivedi S., Buhmann J.M. (2012) Anisotropic ssTEM Image Segmentation Using Dense Correspondence across Sections. In: Ayache N., Delingette H., Golland P., Mori K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7510. Springer, Berlin, Heidelberg
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.
KeywordsMembrane Segmentation Anisotropic Data Dense Correspondence SIFT Flow
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