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Detecting and segmenting object instances is a common task in biomedicalapplications. Examples range from detecting lesions on functional magneticresonance images, to the detection of tumours in histopathological images andextracting quantitative single-cell information from microscopy imagery, wherecell segmentation is a major bottleneck. Attention-based transformers arestate-of-the-art in a range of deep learning fields. They have recently beenproposed for segmentation tasks where they are beginning to outperforming othermethods. We present a novel attention-based cell detection transformer(Cell-DETR) for direct end-to-end instance segmentation. While the segmentationperformance 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 thetypical use case of segmenting yeast in microstructured environments, commonlyemployed in systems or synthetic biology. For the specific use case, theproposed method surpasses the state-of-the-art tools for semantic segmentationand additionally predicts the individual object instances. The fast andaccurate instance segmentation performance increases the experimentalinformation yield for a posteriori data processing and makes online monitoringof experiments and closed-loop optimal experimental design feasible.

Attention-Based Transformers for Instance Segmentation of Cells in Microstructures