Authors:
Khola Naseem
1
;
2
;
Nabeel Khalid
1
;
2
;
Lea Bertgen
3
;
Johannes M. Herrmann
3
;
Andreas Dengel
1
;
2
and
Sheraz Ahmed
1
Affiliations:
1
German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern 67663, Germany
;
2
RPTU Kaiserslautern–Landau, 67663 Kaiserslautern, Germany
;
3
Cell Biology, University of Kaiserslautern, RPTU, Germany
Keyword(s):
Instance Segmentation, Deep Learning, Yeast Cell, Microstructure Environment, Traps, Time-Lapse Fluorescence Microscopy, Synthetic Biology.
Abstract:
Cell segmentation is a crucial task, especially in microstructured environments commonly used in synthetic biology. Segmenting cells in these environments becomes particularly challenging when the cells and the surrounding traps share similar characteristics. While deep learning-based methods have shown success in cell segmentation, limited progress has been made in segmenting yeast cells within such complex environments. Most current approaches rely on traditional machine learning techniques. To address this challenge, the study proposed a transfer-based instance segmentation approach to tackle both cell and trap segmentation in mi-crostructured environments. The attention-based mechanism in the model’s backbone enables a more precise focus on key features, leading to improved segmentation accuracy. The proposed approach outperforms existing state-of-the-art methods, achieving a 5% improvement in terms of Intersection over Union (IoU) for the segmentation of both cells and traps in
microscopic images.
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