Prangemeier, Tim ; Reich, Christoph ; Koeppl, Heinz (2022)
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures.
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020). virtual Conference (16.12.2020-19.12.2020)
doi: 10.26083/tuprints-00021666
Konferenzveröffentlichung, Zweitveröffentlichung, Postprint
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (Abstract)
Detecting and segmenting object instances is a common task in biomedical applications. Examples range from detecting lesions on functional magnetic resonance images, to the detection of tumours in histopathological images and extracting quantitative single-cell information from microscopy imagery, where cell segmentation is a major bottleneck. Attention-based transformers are state-of-the-art in a range of deep learning fields. They have recently been proposed for segmentation tasks where they are beginning to outperform other methods. We present a novel attention-based cell detection transformer (CellDETR) for direct end-to-end instance segmentation. While the segmentation performance is on par with a state-of-the-art instance segmentation method, Cell-DETR is simpler and faster. We showcase the method's contribution in a the typical use case of segmenting yeast in microstructured environments, commonly employed in systems or synthetic biology. For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances. The fast and accurate instance segmentation performance increases the experimental information yield for a posteriori data processing and makes online monitoring of experiments and closed-loop optimal experimental design feasible. Code and data sample is available at https://git.rwth-aachen.de/ bcs/projects/cell-detr.git.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Prangemeier, Tim ; Reich, Christoph ; Koeppl, Heinz |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Attention-Based Transformers for Instance Segmentation of Cells in Microstructures |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2020 |
| Verlag: | IEEE |
| Buchtitel: | 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) |
| Kollation: | 8 Seiten |
| Veranstaltungstitel: | IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 16.12.2020-19.12.2020 |
| DOI: | 10.26083/tuprints-00021666 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21666 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | Detecting and segmenting object instances is a common task in biomedical applications. Examples range from detecting lesions on functional magnetic resonance images, to the detection of tumours in histopathological images and extracting quantitative single-cell information from microscopy imagery, where cell segmentation is a major bottleneck. Attention-based transformers are state-of-the-art in a range of deep learning fields. They have recently been proposed for segmentation tasks where they are beginning to outperform other methods. We present a novel attention-based cell detection transformer (CellDETR) for direct end-to-end instance segmentation. While the segmentation performance is on par with a state-of-the-art instance segmentation method, Cell-DETR is simpler and faster. We showcase the method's contribution in a the typical use case of segmenting yeast in microstructured environments, commonly employed in systems or synthetic biology. For the specific use case, the proposed method surpasses the state-of-the-art tools for semantic segmentation and additionally predicts the individual object instances. The fast and accurate instance segmentation performance increases the experimental information yield for a posteriori data processing and makes online monitoring of experiments and closed-loop optimal experimental design feasible. Code and data sample is available at https://git.rwth-aachen.de/ bcs/projects/cell-detr.git. |
| Freie Schlagworte: | attention, instance segmentation, transformers, single-cell analysis, synthetic biology, microfluidics, deep learning |
| Status: | Postprint |
| URN: | urn:nbn:de:tuda-tuprints-216661 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie |
| 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 |
| Hinterlegungsdatum: | 20 Jul 2022 14:50 |
| Letzte Änderung: | 26 Jul 2022 08:21 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
- Attention-Based Transformers for Instance Segmentation of Cells in Microstructures. (deposited 20 Jul 2022 14:50) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum