The semantic image segmentation task consists of classifying each pixel of an
image into an instance, where each instance corresponds to a class. This task
is a part of the concept of scene understanding or better explaining the global
context of an image. In the medical image analysis domain, image segmentation
can be used for image-guided interventions, radiotherapy, or improved
radiological diagnostics. In this review, we categorize the leading deep
learning-based medical and non-medical image segmentation solutions into six
main groups of deep architectural, data synthesis-based, loss function-based,
sequenced models, weakly supervised, and multi-task methods and provide a
comprehensive review of the contributions in each of these groups. Further, for
each group, we analyze each variant of these groups and discuss the limitations
of the current approaches and present potential future research directions for
semantic image segmentation.

本文对基于深度学习的医学和非医学图像分割解决方案进行归类，并从六个主要类别 —— 深度架构、数据合成、损失函数、顺序模型、弱监督和多任务方法对其进行综合性评估。进一步地，对于每一组，我们分析了这些组合的每一个变种，并讨论了当前方法的局限性和未来研究方向。