Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To mitigate this effect, recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation. If the integration of prior knowledge in image segmentation is not a new topic in classical optimization approaches, it is today an increasing trend in CNN based image segmentation, as shown by the growing literature on the topic. In this survey, we focus on high level prior, embedded at the loss function level. We categorize the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints. We highlight strengths and limitations of current approaches, discuss the challenge related to the design and the integration of prior-based losses, and the optimization strategies, and draw future research directions.

今天，深度卷积神经网络 (CNN) 已经在各种成像模式和任务中展示了监督医学图像分割的最先进性能。尽管早期取得了成功，但分割网络仍可能会产生解剖学异常的分割，在对象边界附近存在漏洞或不准确。为了减轻这种影响，最近的研究工作集中在结合空间信息或先验知识来实施解剖学上合理的分割。如果在图像分割中集成先验知识在经典优化方法中不是一个新话题，那么它在今天是基于 CNN 的图像分割的一个增长趋势，正如关于该主题的越来越多的文献所示。在本次调查中，我们专注于嵌入在损失函数级别的高级先验。我们根据先验的性质对文章进行分类：对象形状、大小、拓扑和区域间约束。我们强调了当前方法的优势和局限性，讨论了与基于先验损失的设计和集成相关的挑战，以及优化策略，并绘制了未来的研究方向。