Attention-Based Transformers for Instance Segmentation of Cells in Microstructures

TL;DR

This paper introduces Cell-DETR, an attention-based transformer model for end-to-end cell instance segmentation in microscopy images, achieving comparable accuracy to state-of-the-art methods but with greater simplicity and speed, especially in yeast microstructure analysis.

Contribution

The paper presents a novel transformer-based model, Cell-DETR, that simplifies and accelerates cell instance segmentation while maintaining high accuracy, specifically applied to yeast in microstructured environments.

Findings

Cell-DETR achieves state-of-the-art segmentation performance.

The method is faster and simpler than existing approaches.

It enables online monitoring and improved data analysis in biological experiments.

Abstract

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.