Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 May:85:107-122.
doi: 10.1016/j.ejmp.2021.05.003. Epub 2021 May 13.

A review of deep learning based methods for medical image multi-organ segmentation

Yabo Fu  1 , Yang Lei  1 , Tonghe Wang  1 , Walter J Curran  1 , Tian Liu  1 , Xiaofeng Yang  2
Affiliations
Review

A review of deep learning based methods for medical image multi-organ segmentation

Yabo Fu et al. Phys Med. 2021 May.

Abstract

Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.

PubMed Disclaimer

Conflict of interest statement

Disclosures

The authors declare no conflicts of interest.

Figures

Fig. 1.
Fig. 1.
The number of publications for DL-based multi-organ segmentation (till October 2020).
Fig. 2.
Fig. 2.
The network components of the pixelwise classification methods.
Fig. 3.
Fig. 3.
The network components of the end-to-end segmentation methods.
See this image and copyright information in PMC

References

    1. Liao ZX, Lee JJ, Komaki R, Gomez DR, O’Reilly MS, Fossella FV, et al. Bayesian Adaptive Randomization Trial of Passive Scattering Proton Therapy and Intensity-Modulated Photon Radiotherapy for Locally Advanced Non-Small-Cell Lung Cancer (vol 36, pg 1813, 2018). Journal of Clinical Oncology. 2018;36:2570-. - PMC - PubMed
    1. Molitoris JK, Diwanji T, Snider JW, Mossahebi S, Samanta S, Badiyan SN, et al. Advances in the use of motion management and image guidance in radiation therapy treatment for lung cancer. Journal of Thoracic Disease. 2018;10:S2437–S50. - PMC - PubMed
    1. Vyfhuis MAL, Onyeuku N, Diwanji T, Mossahebi S, Amin NP, Badiyan SN, et al. Advances in proton therapy in lung cancer. Therapeutic Advances in Respiratory Disease. 2018;12. - PMC - PubMed
    1. Hurkmans CW, Borger JH, Pieters BR, Russell NS, Jansen EP, Mijnheer BJ. Variability in target volume delineation on CT scans of the breast. Int J Radiat Oncol Biol Phys. 2001;50:1366–72. - PubMed
    1. Rasch C, Steenbakkers R, van Herk M. Target definition in prostate, head, and neck. Seminars in Radiation Oncology. 2005;15:136–45. - PubMed