Prangemeier, Tim ; Wildner, Christian ; Françani, André O. ; Reich, Christoph ; Koeppl, Heinz (2022)
Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning.
IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB). Online (27.10.2020-29.10.2020)
doi: 10.26083/tuprints-00021524
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
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Kurzbeschreibung (Abstract)
Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, existing segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the method's contribution to segmenting yeast in microstructured environments with a typical synthetic biology application in mind. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Prangemeier, Tim ; Wildner, Christian ; Françani, André O. ; Reich, Christoph ; Koeppl, Heinz |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Verlag: | IEEE |
| Buchtitel: | 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB) |
| Veranstaltungsort: | Online |
| Veranstaltungsdatum: | 27.10.2020-29.10.2020 |
| DOI: | 10.26083/tuprints-00021524 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21524 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, existing segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the method's contribution to segmenting yeast in microstructured environments with a typical synthetic biology application in mind. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective. |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-215242 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab Interdisziplinäre Forschungsprojekte Interdisziplinäre Forschungsprojekte > Centre for Synthetic Biology |
| Hinterlegungsdatum: | 20 Jul 2022 13:50 |
| Letzte Änderung: | 26 Jul 2022 08:14 |
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- Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning. (deposited 20 Jul 2022 13:50) [Gegenwärtig angezeigt]
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