Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images Journal Article
| Authors: | Widmer, C.; Heinrich, S.; Drewe, P.; Lou, X.; Umrania, S.; Rätsch, G. |
| Article Title: | Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images |
| Abstract: | Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and provides a user-friendly graphical user interface. We show that our tool is as accurate as manual annotation and greatly reduces the time for the registration. |
| Keywords: | fluorescence; artificial intelligence; cell nucleus; anisotropy; image segmentation; biological process; learning systems; automated segmentation; cell nuclei detection; shape reconstruction; nuclear segmentation; 3d fluorescent microscopic data; graphical user interfaces; interface states; 3d shape reconstruction; biological experiments; machine learning techniques |
| Journal Title: | Signal, Image and Video Processing |
| Volume: | 8 |
| Issue: | 1 Suppl. |
| ISSN: | 1863-1703 |
| Publisher: | Springer London |
| Date Published: | 2014-12-01 |
| Start Page: | S41 |
| End Page: | S48 |
| Language: | English |
| DOI: | 10.1007/s11760-014-0694-8 |
| PROVIDER: | scopus |
| PMCID: | PMC4389647 |
| PUBMED: | 25866587 |
| DOI/URL: | |
| Notes: | Export Date: 2 February 2015 -- Source: Scopus |
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