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Review
. 2019 Aug;32(4):582-596.
doi: 10.1007/s10278-019-00227-x.

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Mohammad Hesam Hesamian  1   2 , Wenjing Jia  3 , Xiangjian He  3 , Paul Kennedy  4
Affiliations
Review

Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

Mohammad Hesam Hesamian et al. J Digit Imaging. 2019 Aug.

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.

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Figures

Fig. 1
Fig. 1
The structure of a CNN [20]
Fig. 2
Fig. 2
Orthogonal representation of 3D volume [92]
Fig. 3
Fig. 3
The structure of FCN [50]
Fig. 4
Fig. 4
The structure of the U-Net [62]
Fig. 5
Fig. 5
A residual block of CRN. Residual block may have various number and combination of layers inside, depending on the network design
See this image and copyright information in PMC

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