A survey on shape-constraint deep learning for medical image segmentation.

Since the advent of U-Net, fully convolutional deep neural networks and its many variants have completely changed the modern landscape of deep-learning based medical image segmentation. However, the over-dependence of these methods on pixel-level classification and regression has been identified early on as a problem. Especially when trained on medical databases with sparse available annotation, these methods are prone to generate segmentation artifacts such as fragmented structures, topological inconsistencies and islands of pixel. These artifacts are especially problematic in medical imaging since segmentation is almost always a pre-processing step for some downstream evaluations like surgical planning, visualization, prognosis, or treatment planning. However, one common thread across all these downstream tasks is the demand of anatomical consistency. To ensure the segmentation result is anatomically consistent, approaches based on Markov/ Conditional Random Fields, Statistical Shape Models, and Active Contours are becoming increasingly popular over the past 5 years. In this review paper, a broad overview of recent literature on bringing anatomical constraints for medical image segmentation is given, the shortcomings and opportunities are discussed and potential future work is elaborated. We review the most relevant papers published until the submission date and provide a tabulated view with method details for quick access.