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.

中文翻译：

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自然和医学图像的深度语义分割：综述

语义图像分割任务包括将图像的每个像素分类为一个实例，其中每个实例对应一个类。此任务是场景理解概念的一部分，或者更好地解释图像的全局上下文。在医学图像分析领域，图像分割可用于图像引导干预、放射治疗或改进的放射诊断。在这篇综述中，我们将领先的基于深度学习的医学和非医学图像分割解决方案分为六大组：深度架构、基于数据合成、基于损失函数、序列模型、弱监督和多任务方法，以及对每个组的贡献进行全面审查。此外，对于每个组，