Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation
This work addresses the labor-intensive annotation problem for biomedical researchers, offering a low-cost solution for cell segmentation.
The paper tackles the high annotation cost of cell image segmentation by proposing an active learning framework using bounding box annotations, which reduces data annotation time by over 90% compared to mask-supervised methods.
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.