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.

我们提出了一种使用边界框注释的主动学习框架，用于细胞分割，大大降低了数据注释成本。通过将YOLOv8检测器与Segment Anything Model (SAM)相结合，我们生成了一个箱子监督学习方法（称为YOLO-SAM），有效降低了数据注释的复杂性。此外，它还集成到一个主动学习框架中，使用MC DropBlock方法以较少的框注释样本来训练分割模型。广泛的实验证明，与遮罩监督深度学习方法相比，我们的模型节省了超过90％的数据注释时间。

利用边界框标注实现低成本细胞图像分割的主动学习