| First Authors | Mangal Prakash |
|---|---|
| Authors | Mangal Prakash, Tim-Oliver Buchholz, Manan Lalit, Pavel Tomancak, Florian Jug, Alexander Krull |
| Corresponding Authors | |
| Last Authors | Alexander Krull |
| Conference Proceedings Volume Title | IEEE ISBI 2020 : International Conference on Biomedical Imaging : April 2-7, 2020, Iowa City, Iowa, USA : symposium proceeding |
| Series Title | IEEE International Symposium on Biomedical Imaging |
| Conference Name | IEEE 17th International Symposium on Biomedical Imaging (ISBI) |
| Conference Location | Iowa City, Iowa, USA |
| Conference Start Date | 0020-04-01 |
| Conference End Date | 0020-04-05 |
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| Publisher | IEEE |
| Conference Proceedings Editors | |
| ISBN | 978-1-5386-9330-8 |
| First Page | 428 |
| Last Page | 432 |
| Open Access | false |
| Print Publication Date | 2020-05-22 |
| Online Publication Date | 2020-05-22 |
| Abstract | Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copious amounts of annotated training data that is for biomedical projects typically not available and excessively expensive to generate. Additionally, tasks become harder in the presence of noise, requiring even more high-quality training data. Hence, we propose to use denoising networks to improve the performance of other DL-based image segmentation methods. More specifically, we present ideas on how state-of-the-art self-supervised CARE networks can improve cell/nuclei segmentation in microscopy data. Using two state-of-the-art baseline methods, U-Net and StarDist, we show that our ideas consistently improve the quality of resulting segmentations, especially when only limited training data for noisy micrographs are available. |
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| Affiliated With | CSBD, Jug, Tomancak |
| Selected By | |
| Acknowledged Services | Scientific Computing Facility |
| Publication Status | Published |
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| DOI | 10.1109/ISBI45749.2020.9098559 |
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| Created By | thuem |
| Added Date | 2021-02-03 |
| Last Edited By | herbst |
| Last Edited Date | 2021-06-21 17:28:13.177 |
| Library ID | 7929 |
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| Entry Complete | true |
| eDoc Compliant | true |
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