Journal
2020 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE
Volume -, Issue -, Pages 700-707Publisher
IEEE COMPUTER SOC
DOI: 10.1109/BIBM49941.2020.9313305
Keywords
attention; instance segmentation; transformers; single-cell analysis; synthetic biology; microfluidics; deep learning
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Funding
- Landesoffensive fur wissenschaftliche Exzellenz as part of the LOEWE Schwerpunkt CompuGene
- European Research Council (ERC) [773196]
- European Research Council (ERC) [773196] Funding Source: European Research Council (ERC)
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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 (Cell-DETR) 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.
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