利用边界框标注实现低成本细胞图像分割的主动学习方法
Active Learning Enabled Low-cost Cell Image Segmentation Using Bounding Box Annotation
AI总结：
本文提出结合YOLOv8与SAM的YOLO-SAM边界框监督方法，并集成MC DropBlock主动学习框架，以极低标注成本实现细胞图像分割，实验显示可节省90%以上标注时间。
摘要：
细胞图像分割通常采用全监督深度学习方法实现，这类方法严重依赖大量标注训练数据。然而，由于细胞形态的复杂性以及对专业知识的要求，细胞图像的像素级标注已成为一项高度劳动密集的任务。为解决上述问题，我们提出了一种使用边界框标注的细胞分割主动学习框架，该框架大幅降低了细胞分割算法的数据标注成本。首先，我们通过将YOLOv8检测器与Segment Anything Model（SAM）相结合，生成了一种边界框监督学习方法（记作YOLO-SAM），有效降低了数据标注的复杂度。此外，该方法被集成到一个主动学习框架中，该框架采用MC DropBlock方法，以更少的边界框标注样本训练分割模型。大量实验表明，与掩码监督深度学习方法相比，我们的模型节省了超过百分之九十的数据标注时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
