In recent years, deep learning (DL) methods have become powerful tools for
biomedical image segmentation. However, high annotation efforts and costs are
commonly needed to acquire sufficient biomedical training data for DL models.
To alleviate the burden of manual annotation, in this paper, we propose a new
weakly supervised DL approach for biomedical image segmentation using boxes
only annotation. First, we develop a method to combine graph search (GS) and DL
to generate fine object masks from box annotation, in which DL uses box
annotation to compute a rough segmentation for GS and then GS is applied to
locate the optimal object boundaries. During the mask generation process, we
carefully utilize information from box annotation to filter out potential
errors, and then use the generated masks to train an accurate DL segmentation
network. Extensive experiments on gland segmentation in histology images, lymph
node segmentation in ultrasound images, and fungus segmentation in electron
microscopy images show that our approach attains superior performance over the
best known state-of-the-art weakly supervised DL method and is able to achieve
(1) nearly the same accuracy compared to fully supervised DL methods with far
less annotation effort, (2) significantly better results with similar
annotation time, and (3) robust performance in various applications.

本文提出一种基于盒子注释的弱监督深度学习方法，通过结合图形搜索和深度学习的方法从盒注释中生成精细的对象掩模，并且使用生成的掩模来训练准确的深度学习分割网络，实验结果表明，该方法优于目前已知的最佳弱监督深度学习方法，并且能够以远少于制备全监督深度学习方法相同的注释成本来实现几乎相同的准确性，而且能够有效地应用于各种应用领域。