Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

Overview

Journal Diagnostics (Basel)

Specialty Radiology

Date 2021 Nov 27

PMID 34829310

Citations 22

Authors

Reza Kalantar

Gigin Lin

Jessica M Winfield

Christina Messiou

Susan Lalondrelle

Matthew D Blackledge

Dow-Mu Koh

Affiliations

Soon will be listed here.

Abstract

The recent rise of deep learning (DL) and its promising capabilities in capturing non-explicit detail from large datasets have attracted substantial research attention in the field of medical image processing. DL provides grounds for technological development of computer-aided diagnosis and segmentation in radiology and radiation oncology. Amongst the anatomical locations where recent auto-segmentation algorithms have been employed, the pelvis remains one of the most challenging due to large intra- and inter-patient soft-tissue variabilities. This review provides a comprehensive, non-systematic and clinically-oriented overview of 74 DL-based segmentation studies, published between January 2016 and December 2020, for bladder, prostate, cervical and rectal cancers on computed tomography (CT) and magnetic resonance imaging (MRI), highlighting the key findings, challenges and limitations.

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References

OConnor J, Aboagye E, Adams J, Aerts H, Barrington S, Beer A . Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2016; 14(3):169-186. PMC: 5378302. DOI: 10.1038/nrclinonc.2016.162. View

Song Y, Hu J, Wu Q, Xu F, Nie S, Zhao Y . Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy. Radiother Oncol. 2020; 145:186-192. DOI: 10.1016/j.radonc.2020.01.020. View

Ghavami N, Hu Y, Gibson E, Bonmati E, Emberton M, Moore C . Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Med Image Anal. 2019; 58:101558. PMC: 7985677. DOI: 10.1016/j.media.2019.101558. View

Lin Y, Lin G, Hong J, Lin Y, Chen F, Ng S . Diffusion radiomics analysis of intratumoral heterogeneity in a murine prostate cancer model following radiotherapy: Pixelwise correlation with histology. J Magn Reson Imaging. 2017; 46(2):483-489. DOI: 10.1002/jmri.25583. View

Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J . Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. J Med Internet Res. 2021; 23(7):e26151. PMC: 8314151. DOI: 10.2196/26151. View

Wong J, Fong A, McVicar N, Smith S, Giambattista J, Wells D . Comparing deep learning-based auto-segmentation of organs at risk and clinical target volumes to expert inter-observer variability in radiotherapy planning. Radiother Oncol. 2019; 144:152-158. DOI: 10.1016/j.radonc.2019.10.019. View

Zhu Y, Wang L, Liu M, Qian C, Yousuf A, Oto A . MRI-based prostate cancer detection with high-level representation and hierarchical classification. Med Phys. 2017; 44(3):1028-1039. PMC: 5540150. DOI: 10.1002/mp.12116. View

Schob S, Meyer H, Pazaitis N, Schramm D, Bremicker K, Exner M . ADC Histogram Analysis of Cervical Cancer Aids Detecting Lymphatic Metastases-a Preliminary Study. Mol Imaging Biol. 2017; 19(6):953-962. DOI: 10.1007/s11307-017-1073-y. View

McVeigh P, Syed A, Milosevic M, Fyles A, Haider M . Diffusion-weighted MRI in cervical cancer. Eur Radiol. 2008; 18(5):1058-64. DOI: 10.1007/s00330-007-0843-3. View

10.

Nie D, Wang L, Gao Y, Lian J, Shen D . STRAINet: Spatially Varying sTochastic Residual AdversarIal Networks for MRI Pelvic Organ Segmentation. IEEE Trans Neural Netw Learn Syst. 2018; 30(5):1552-1564. PMC: 6550324. DOI: 10.1109/TNNLS.2018.2870182. View

11.

Guo Y, Gao Y, Shen D . Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching. IEEE Trans Med Imaging. 2015; 35(4):1077-89. PMC: 5002995. DOI: 10.1109/TMI.2015.2508280. View

12.

Cheng R, Roth H, Lay N, Lu L, Turkbey B, Gandler W . Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J Med Imaging (Bellingham). 2017; 4(4):041302. PMC: 5565676. DOI: 10.1117/1.JMI.4.4.041302. View

13.

Ma L, Guo R, Zhang G, Tade F, Schuster D, Nieh P . Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. Proc SPIE Int Soc Opt Eng. 2018; 10133. PMC: 6138461. DOI: 10.1117/12.2255755. View

14.

Veera J, Lim K, Dowling J, OConnor C, Holloway L, Vinod S . Dedicated MRI simulation for cervical cancer radiation treatment planning: Assessing the impact on clinical target volume delineation. J Med Imaging Radiat Oncol. 2018; 63(2):236-243. DOI: 10.1111/1754-9485.12831. View

15.

Brouwer C, Steenbakkers R, van den Heuvel E, Duppen J, Navran A, Bijl H . 3D Variation in delineation of head and neck organs at risk. Radiat Oncol. 2012; 7:32. PMC: 3337234. DOI: 10.1186/1748-717X-7-32. View

16.

Muller B, Futterer J, Gupta R, Katz A, Kirkham A, Kurhanewicz J . The role of magnetic resonance imaging (MRI) in focal therapy for prostate cancer: recommendations from a consensus panel. BJU Int. 2013; 113(2):218-27. PMC: 4131409. DOI: 10.1111/bju.12243. View

17.

Jaffe C . Measures of response: RECIST, WHO, and new alternatives. J Clin Oncol. 2006; 24(20):3245-51. DOI: 10.1200/JCO.2006.06.5599. View

18.

Duan C, Liang Z, Bao S, Zhu H, Wang S, Zhang G . A coupled level set framework for bladder wall segmentation with application to MR cystography. IEEE Trans Med Imaging. 2010; 29(3):903-15. PMC: 2894540. DOI: 10.1109/TMI.2009.2039756. View

19.

Miles E, Clark C, Guerrero Urbano M, Bidmead M, Dearnaley D, Harrington K . The impact of introducing intensity modulated radiotherapy into routine clinical practice. Radiother Oncol. 2005; 77(3):241-6. DOI: 10.1016/j.radonc.2005.10.011. View

20.

Makni N, Iancu A, Colot O, Puech P, Mordon S, Betrouni N . Zonal segmentation of prostate using multispectral magnetic resonance images. Med Phys. 2011; 38(11):6093-105. DOI: 10.1118/1.3651610. View