Document worth reading: “Deep Semantic Segmentation of Natural and Medical Images: A Review”

The (medical) image semantic segmentation job consists of classifying each pixel of an image (or just a quantity of ones) into an event, the place each event (or class) equal to a class. This job is a component of the thought of scene understanding or greater explaining the worldwide context of an image. In the medical image analysis space, image segmentation may be utilized for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this overview, we categorize the first deep learning-based medical and non-medical image segmentation choices into six main groups of deep architectural enhancements, data synthesis-based, loss function-based enhancements, sequenced fashions, weakly supervised, and multi-task methods and further for each group we analyzed each variant of these groups and give attention to limitations of the current approaches and future evaluation directions for semantic image segmentation. Deep Semantic Segmentation of Natural and Medical Images: A Review