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Attention-Based Transformers for Instance Segmentation of Cells in Microstructures

Prangemeier, Tim ; Reich, Christoph ; Koeppl, Heinz (2022):
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures. (Postprint)
In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 700-707,
Darmstadt, IEEE, IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020), virtual Conference, 16.-19.12.2020, ISBN 978-1-7281-6215-7,
DOI: 10.26083/tuprints-00021666,
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Item Type: Conference or Workshop Item
Origin: Secondary publication service
Status: Postprint
Title: Attention-Based Transformers for Instance Segmentation of Cells in Microstructures
Language: English
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.

Book Title: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Place of Publication: Darmstadt
Publisher: IEEE
Collation: 8 Seiten
Uncontrolled Keywords: attention, instance segmentation, transformers, single-cell analysis, synthetic biology, microfluidics, deep learning
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Event Title: IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020)
Event Location: virtual Conference
Event Dates: 16.-19.12.2020
Date Deposited: 20 Jul 2022 14:50
Last Modified: 20 Jul 2022 14:50
DOI: 10.26083/tuprints-00021666
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-216661
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21666
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