FastSME: Faster and Smoother Manifold Extraction From 3D Stack

Sreetama Basu, Elton Rexhepaj, Nathalie Spassky, Auguste Genovesio, Rasmus Reinhold Paulsen, ASM Shihavuddin; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2281-2289

Abstract


3D image stacks are routinely acquired to capture data that lie on undulating 3D manifolds yet processed in 2D by biologists. Algorithms to reconstruct the specimen morphology into a 2D representation from the 3D image volume are employed in such scenarios. In this paper, we present FastSME, which offers several improvements on the baseline SME algorithm which enables accurate 2D representation of data on a manifold from 3D volumes, however is computationally expensive. The improvements are achieved in terms of processing speed (3X-10X speed-up depending on image size), minimizing sensitivity to initialization, and also increases local smoothness of the recovered manifold resulting in better reconstructed 2D composite image. We compare the proposed FastSME against the baseline SME as well as other accessible state-of-the-art tools on synthetic and real microscopy data. Our evaluation on multiple metrics demonstrates the efficiency of the presented method in maintaining fidelity of manifold shape and hence specimen morphology.

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[bibtex]
@InProceedings{Basu_2018_CVPR_Workshops,
author = {Basu, Sreetama and Rexhepaj, Elton and Spassky, Nathalie and Genovesio, Auguste and Reinhold Paulsen, Rasmus and Shihavuddin, ASM},
title = {FastSME: Faster and Smoother Manifold Extraction From 3D Stack},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}