# Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Review · Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul J Kennedy

- Archive id: doi:10.1007/s10278-019-00227-x
- Published: 2019-05-29
- Venue: Journal of Imaging Informatics in Medicine
- DOI: https://doi.org/10.1007/s10278-019-00227-x
- PMID: 31144149
- Citations: 1,638
- Source URL: https://doi.org/10.1007/s10278-019-00227-x
- PDF: https://link.springer.com/content/pdf/10.1007/s10278-019-00227-x.pdf
- Provenance: openalex

## 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 reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI in Clinical Medicine](https://space-kitty.com/topics/ai-medicine)

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