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Attention-Based Transformers for Instance Segmentation of Cells in Microstructures

Prangemeier, Tim and Reich, C. and Koeppl, H. (2020):
Attention-Based Transformers for Instance Segmentation of Cells in Microstructures.
IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020), virtual Conference, 16.-19.12., [Conference or Workshop Item]

Abstract

Detecting and segmenting object instances is acommon 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-basedtransformers are state-of-the-art in a range of deep learningfields. They have recently been proposed for segmentation taskswhere they are beginning to outperforming other methods. We present a novel attention-basedcell detection transformer(Cell-DETR) for direct end-to-end instance segmentation. While the segmentation performance is on par with a state-of-the-artinstance segmentation method, Cell-DETR is simpler and faster.We showcase the method’s contribution in a the typical use caseof segmenting yeast in microstructured environments, commonly employed in systems or synthetic biology. For the specific usecase, the proposed method surpasses the state-of-the-art tools forsemantic segmentation and additionally predicts the individual object instances. The fast and accurate instance segmentation performance increases the experimental information yield for a posterioridata processing and makes online monitoring of experiments and closed-loop optimal experimental design feasible.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Prangemeier, Tim and Reich, C. and Koeppl, H.
Title: Attention-Based Transformers for Instance Segmentation of Cells in Microstructures
Language: English
Abstract:

Detecting and segmenting object instances is acommon 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-basedtransformers are state-of-the-art in a range of deep learningfields. They have recently been proposed for segmentation taskswhere they are beginning to outperforming other methods. We present a novel attention-basedcell detection transformer(Cell-DETR) for direct end-to-end instance segmentation. While the segmentation performance is on par with a state-of-the-artinstance segmentation method, Cell-DETR is simpler and faster.We showcase the method’s contribution in a the typical use caseof segmenting yeast in microstructured environments, commonly employed in systems or synthetic biology. For the specific usecase, the proposed method surpasses the state-of-the-art tools forsemantic segmentation and additionally predicts the individual object instances. The fast and accurate instance segmentation performance increases the experimental information yield for a posterioridata processing and makes online monitoring of experiments and closed-loop optimal experimental design feasible.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Event Title: IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2020)
Event Location: virtual Conference
Event Dates: 16.-19.12.
Date Deposited: 05 Nov 2020 12:56
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