Open AccessMedicineComputer Science

M. H. Hesamian, W. Jia, Xiangjian He, Paul J. Kennedy

2019.5.29JOURNAL OF DIGITAL IMAGING

DOI: 10.1007/s10278-019-00227-x

tlooto Summary

A critical appraisal of popular methods that have employed deep learning techniques for medical image segmentation is presented and the most common challenges incurred are summarized and suggest possible solutions.

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.

Citation format

HESAMIAN, M. H., et al. Deep learning techniques for medical image segmentation: Achievements and challenges. JOURNAL OF DIGITAL IMAGING, 2019, 32: 582–596.