Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Mohammad Hesam Hesamian, Wenjing Jia, Xiangjian He, Paul Kennedy

Research output: Journal PublicationArticlepeer-review

963 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)582-596
Number of pages15
JournalJournal of Digital Imaging
Issue number4
Publication statusPublished - 15 Aug 2019
Externally publishedYes


  • CNN
  • Deep learning
  • Medical image segmentation
  • Organ segmentation

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications


Dive into the research topics of 'Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges'. Together they form a unique fingerprint.

Cite this