  Segmentation is a fundamental process in microscopic cell image analysis.
With the advent of recent advances in deep learning, more accurate and
high-throughput cell segmentation has become feasible. However, most existing
deep learning-based cell segmentation algorithms require fully annotated
ground-truth cell labels, which are time-consuming and labor-intensive to
generate. In this paper, we introduce Scribble2Label, a novel weakly-supervised
cell segmentation framework that exploits only a handful of scribble
annotations without full segmentation labels. The core idea is to combine
pseudo-labeling and label filtering to generate reliable labels from weak
supervision. For this, we leverage the consistency of predictions by
iteratively averaging the predictions to improve pseudo labels. We demonstrate
the performance of Scribble2Label by comparing it to several state-of-the-art
cell segmentation methods with various cell image modalities, including
bright-field, fluorescence, and electron microscopy. We also show that our
method performs robustly across different levels of scribble details, which
confirms that only a few scribble annotations are required in real-use cases.
