![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
Tools
Tools
Preview |
PDF (Accepted manuscript (final draft))
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2MB |
| Item Type: | Article |
|---|---|
| Title: | A benchmark for epithelial cell tracking |
| Creators Name: | Funke, J., Mais, L., Champion, A., Dye, N. and Kainmueller, D. |
| Abstract: | Segmentation and tracking of epithelial cells in light microscopy (LM) movies of developing tissue is an abundant task in cell- and developmental biology. Epithelial cells are densely packed cells that form a honeycomb-like grid. This dense packing distinguishes membrane-stained epithelial cells from the types of objects recent cell tracking benchmarks have focused on, like cell nuclei and freely moving individual cells. While semi-automated tools for segmentation and tracking of epithelial cells are available to biologists, common tools rely on classical watershed based segmentation and engineered tracking heuristics, and entail a tedious phase of manual curation. However, a different kind of densely packed cell imagery has become a focus of recent computer vision research, namely electron microscopy (EM) images of neurons. In this work we explore the benefits of two recent neuron EM segmentation methods for epithelial cell tracking in light microscopy. In particular we adapt two different deep learning approaches for neuron segmentation, namely Flood Filling Networks and MALA, to epithelial cell tracking. We benchmark these on a dataset of eight movies with up to 200 frames. We compare to Moral Lineage Tracing, a combinatorial optimization approach that recently claimed state of the art results for epithelial cell tracking. Furthermore, we compare to Tissue Analyzer, an off-the-shelf tool used by Biologists that serves as our baseline. |
| Source: | Lecture Notes in Computer Science |
| ISSN: | 0302-9743 |
| Publisher: | Springer |
| Volume: | 11134 |
| Page Range: | 437-445 |
| Date: | 23 January 2019 |
| Additional Information: | Copyright © Springer Nature Switzerland AG 2019 |
| Official Publication: | https://doi.org/10.1007/978-3-030-11024-6_33 |
Repository Staff Only: item control page
