  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 outperforming 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.
