| First Authors | |
|---|---|
| Authors | Martin Maska, Tereza Necasova, David Wiesner, Dmitry V. Sorokin, Igor Peterlik, Vladimir Ulman, David Svoboda |
| Corresponding Authors | |
| Last Authors | David Svoboda |
| Conference Proceedings Volume Title | 2019 IEEE International Conference on Image Processing : proceedings : September 22-25, 2019, Taipei International Convention Center (TICC), Taipei, Taiwan |
| Series Title | |
| Conference Name | 26th IEEE International Conference on Image Processing (ICIP) |
| Conference Location | Taipei, Taiwan |
| Conference Start Date | 2019-09-22 |
| Conference End Date | 2019-09-25 |
| Chapter Number | |
| Publisher | IEEE |
| Conference Proceedings Editors | |
| ISBN | 978-1-5386-6249-6 |
| First Page | 819 |
| Last Page | 823 |
| Open Access | false |
| Print Publication Date | 2019-09-26 |
| Online Publication Date | |
| Abstract | Development, parameter tuning, and objective benchmarking of bioimage analysis workflows heavily rely on the availability of diverse bioimage datasets accompanied by reference annotations. In this paper, we present a new benchmark dataset, FiloData3D, designed for in-depth performance assessments of fully 3D filopodium segmentation and tracking algorithms that emerged recently in the field. It consists of 180 synthetic, fully annotated, 3D time-lapse sequences of single lung cancer cells, combining different cell shapes, signal-to-noise ratios, and anisotropy ratios, which are the well-known factors that influence the quality of segmentation and tracking results. Using FiloData3D, we show that the number of filopodia and their lengths extracted are significantly underestimated in the case of traditional 2D protocols that prevail in daily practice compared to fully 3D measurements, calling for a procedural change in filopodial analyses of 3D+t bioimage data. |
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| Affiliated With | Postdocs, Tomancak |
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| Publication Status | Published |
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| DOI | 10.1109/ICIP.2019.8803721 |
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| Created By | thuem |
| Added Date | 2020-04-22 |
| Last Edited By | thuem |
| Last Edited Date | 2020-05-19 14:24:51.645 |
| Library ID | 7654 |
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