YeastFormer: An End-to-End Instance Segmentation Approach for Yeast Cells in Microstructure Environment

Khola Naseem, Nabeel Khalid, Lea Bertgen, Johannes M. Herrmann, Andreas Dengel, Sheraz Ahmed

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