Image segmentation for human organs is an important task for the diagnosis and treatment of diseases. Current deep learning-based methods are fully supervised and need pixel-level labels. Since the medical images are highly specialized and complex, the work of delineating pixel-level segmentation masks is very time-consuming. Weakly supervised methods are then chosen to lighten the workload, which only needs physicians to determine whether an image contains the organ regions of interest. These weakly supervised methods have a common drawback, in that they do not incorporate prior knowledge that alleviates the lack of pixel-level information for segmentation. In this work, we propose a weakly supervised method based on prior knowledge for the segmentation of human organs. The proposed method was validated on three data sets of human organ segmentation. Experimental results show that the proposed image-level supervised segmentation method outperforms several state-of-the-art methods.

人体器官的图像分割是疾病诊断和治疗的一项重要任务。当前基于深度学习的方法是完全监督的，需要像素级标签。由于医学图像是高度专业化和复杂的，描绘像素级分割掩模的工作非常耗时。然后选择弱监督方法来减轻工作量，这只需要医生确定图像是否包含感兴趣的器官区域。这些弱监督方法有一个共同的缺点，因为它们没有结合先验知识，从而缓解了分割像素级信息的缺乏。在这项工作中，我们提出了一种基于先验知识的弱监督方法来分割人体器官。所提出的方法在人体器官分割的三个数据集上进行了验证。实验结果表明，所提出的图像级监督分割方法优于几种最先进的方法。