Deep Semantic Segmentation of Natural and Medical Images: A Review
It provides a comprehensive overview for researchers and practitioners in computer vision and medical imaging, but it is incremental as it synthesizes existing work without introducing new methods.
This review categorizes deep learning-based semantic segmentation methods for both natural and medical images into six groups, analyzing each variant and discussing current limitations and future research directions.
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.