Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
This is a systematic review that provides a taxonomy and analysis for researchers in medical image analysis, identifying challenges like the need for standardized benchmarks.
This paper tackles the problem of efficiently annotating medical images by reviewing deep interactive segmentation methods, finding that 121 methods have been proposed in the medical imaging domain, but there is a severe lack of comparison across them.
Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks.