Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Abstract

Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

Abstract of "Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges" by Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He et al., https://doi.org/10.1007/s10278-019-00227-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.