Conference paper
FastSME: faster and smoother manifold extraction from 3D stack
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2018 |
| Pages: | 2362-23628 |
| Proceedings: | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
| ISBN: | 1538661004 , 1538661012 , 9781538661000 and 9781538661017 |
| ISSN: | 21607516 and 21607508 |
| Types: | Conference paper |
| DOI: | 10.1109/CVPRW.2018.00305 |
| ORCIDs: | Paulsen, Rasmus Reinhold and Shihavuddin, ASM |
2D composite image 3D image stacks 3D image volume 3D manifolds Biology FastSME Indexes Manifolds Microscopy Optimization Three-dimensional displays Two dimensional displays baseline SME algorithm biology computing capture data feature extraction image reconstruction image representation image size manifold extraction manifold shape microscopy data specimen morphology
