  Cell image segmentation is usually implemented using fully supervised deep
learning methods, which heavily rely on extensive annotated training data. Yet,
due to the complexity of cell morphology and the requirement for specialized
knowledge, pixel-level annotation of cell images has become a highly
labor-intensive task. To address the above problems, we propose an active
learning framework for cell segmentation using bounding box annotations, which
greatly reduces the data annotation cost of cell segmentation algorithms.
First, we generate a box-supervised learning method (denoted as YOLO-SAM) by
combining the YOLOv8 detector with the Segment Anything Model (SAM), which
effectively reduces the complexity of data annotation. Furthermore, it is
integrated into an active learning framework that employs the MC DropBlock
method to train the segmentation model with fewer box-annotated samples.
Extensive experiments demonstrate that our model saves more than ninety percent
of data annotation time compared to mask-supervised deep learning methods.
