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.
@article{arxiv.2311.13964,
title = {Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy},
author = {Zdravko Marinov and Paul F. Jäger and Jan Egger and Jens Kleesiek and Rainer Stiefelhagen},
journal= {arXiv preprint arXiv:2311.13964},
year = {2024}
}
Comments
26 pages, 8 figures, 10 tables; Zdravko Marinov and Paul F. J\"ager and co-first authors; This work has been submitted to the IEEE for possible publication