Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

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

doi:10.1007/s10278-019-00227-x

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

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

^*Corresponding author for this work

Research output: Journal Publication › Article › peer-review

1548 Citations (Scopus)

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.

Original language	English
Pages (from-to)	582-596
Number of pages	15
Journal	Journal of Digital Imaging
Volume	32
Issue number	4
DOIs	https://doi.org/10.1007/s10278-019-00227-x
Publication status	Published - 15 Aug 2019
Externally published	Yes

Free Keywords

CNN
Deep learning
Medical image segmentation
Organ segmentation

ASJC Scopus subject areas

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

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10.1007/s10278-019-00227-x

Cite this

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title = "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.",

keywords = "CNN, Deep learning, Medical image segmentation, Organ segmentation",

author = "Hesamian, \{Mohammad Hesam\} and Wenjing Jia and Xiangjian He and Paul Kennedy",

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AU - Kennedy, Paul

PY - 2019/8/15

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N2 - 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.

AB - 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.

KW - CNN

KW - Deep learning

KW - Medical image segmentation

KW - Organ segmentation

UR - https://www.scopus.com/pages/publications/85066806685

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ER -

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Abstract

Free Keywords

ASJC Scopus subject areas

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