Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Radiation Oncology
Published in: Radiation Oncology 1/2019

Open Access 01-12-2019 | Research

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer

Authors: Sang Hee Ahn, Adam Unjin Yeo, Kwang Hyeon Kim, Chankyu Kim, Youngmoon Goh, Shinhaeng Cho, Se Byeong Lee, Young Kyung Lim, Haksoo Kim, Dongho Shin, Taeyoon Kim, Tae Hyun Kim, Sang Hee Youn, Eun Sang Oh, Jong Hwi Jeong

Published in: Radiation Oncology | Issue 1/2019

Login to get access

Abstract

Background

Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability.
This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer.

Methods

On-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures.

Results

The atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively.

Conclusions

In this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.
Literature
1.
Vinod SK, Jameson MG, Min M, Holloway LC. Uncertainties in volume delineation in radiation oncology: a systematic review and recommendations for future studies. Radiother Oncol. 2016;121(2):169–79.CrossRef
2.
Chen A, Deeley MA, Niermann KJ, Moretti L, Dawant BM. Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images. Med Phys. 2010;37(12):6338–46.CrossRef
3.
Qazi AA, Pekar V, Kim J, Xie J, Breen SL, Jaffray DA. Auto-segmentation of normal and target structures in head and neck CT images: a feature-driven model-based approach. Med Phys. 2011;38(11):6160–70.CrossRef
4.
Xu Y, Xu C, Kuang X, Wang H, Chang EI, Huang W, et al. 3D-SIFT-flow for atlas-based CT liver image segmentation. Med Phys. 2016;43(5):2229–41.CrossRef
5.
Daisne J-F, Blumhofer A. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation. Radiat Oncol. 2013;8(1):154.CrossRef
6.
Sjöberg C, Lundmark M, Granberg C, Johansson S, Ahnesjö A, Montelius A. Clinical evaluation of multi-atlas based segmentation of lymph node regions in head and neck and prostate cancer patients. Radiat Oncol. 2013;8(1):229.CrossRef
7.
Thomson D, Boylan C, Liptrot T, Aitkenhead A, Lee L, Yap B, et al. Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk. Radiat Oncol. 2014;9(1):173.CrossRef
8.
Karabatak M, Ince MC. An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl. 2009;36(2):3465–9.CrossRef
9.
Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199–210.CrossRef
10.
Übeyli ED. Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl. 2007;33(4):1054–62.CrossRef
11.
Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, et al. Deep learning in medical imaging and radiation therapy. Med Phys. 2019;46(1):e1–e36.CrossRef
12.
Kang J, Schwartz R, Flickinger J, Beriwal S. Machine learning approaches for predicting radiation therapy outcomes: a clinician's perspective. Int J Radiat Oncol Biol Phys. 2015;93(5):1127–35.CrossRef
13.
Poynton M, Choi B, Kim Y, Park I, Noh G, Hong S, et al. Machine learning methods applied to pharmacokinetic modelling of remifentanil in healthy volunteers: a multi-method comparison. J Int Med Res. 2009;37(6):1680–91.CrossRef
14.
15.
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems; 2012. p. 1097–105.
16.
Hu P, Wu F, Peng J, Liang P, Kong D. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol. 2016;61(24):8676.CrossRef
17.
Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. In: Annual conference on medical image understanding and analysis. Cham: Springer; 2017. p. 506–17.CrossRef
18.
Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys. 2017;44(10):5221–33.CrossRef
19.
Tong N, Gou S, Yang S, Ruan D, Sheng K. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med Phys. 2018;45(10):4558–67.CrossRef
20.
Yoon HJ, Jeong YJ, Kang H, Jeong JE, Kang DY. Medical image analysis using artificial intelligence. Progress Med Phys. 2019;30(2):49–58.CrossRef
21.
Lustberg T, van Soest J, Gooding M, Peressutti D, Aljabar P, van der Stoep J, et al. Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer. Radiother Oncol. 2018;126(2):312–7.CrossRef
22.
Keras CF. The Python deep learning library. In: Astrophysics Source Code Library; 2018.
23.
24.
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: In: international conference on medical image computing and computer-assisted intervention. Cham: Springer; 2015. p. 234–41.
25.
Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML–10); 2010. p. 807–14.
26.
27.
28.
Roth HR, Shen C, Oda H, Oda M, Hayashi Y, Misawa K, Mori K. Deep learning and its application to medical image segmentation. Medical Imaging Technology. 2018;36(2):63–71.
29.
Kingma DP, Adam BJ. A method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations (ICLR 2015); 2015.
30.
Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15:29.CrossRef
31.
Sharp G, Fritscher KD, Pekar V, Peroni M, Shusharina N, Veeraraghavan H, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;41(5):050902.CrossRef
32.
Christ PF, Ettlinger F, Grün F, Elshaera MEA, Lipkova J, Schlecht S, Rempfler M. Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv:1702.05970; 2017.
33.
La Macchia M, Fellin F, Amichetti M, Cianchetti M, Gianolini S, Paola V, et al. Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancer. Radiat Oncol. 2012;7(1):160.CrossRef
34.
Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Trans Med Imaging. 2018;37(8):1822–34.CrossRef
35.
Kim H, Monroe JI, Lo S, Yao M, Harari PM, Machtay M, et al. Quantitative evaluation of image segmentation incorporating medical consideration functions. Med Phys. 2015;42(6 Part 1):3013–23.CrossRef
36.
Milletari F, Navab N, Ahmadi SA. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV); 2016. p. 565–71. IEEE.CrossRef
37.
Loi G, Fusella M, Lanzi E, Cagni E, Garibaldi C, Iacoviello G, et al. Performance of commercially available deformable image registration platforms for contour propagation using patient-based computational phantoms: a multi-institutional study. Med Phys. 2018;45(2):748–5.CrossRef
38.
Jiang W, Liu Z, Lee KH, Chen S, Ng YL, Dou Q, et al. Respiratory motion correction in abdominal MRI using a densely connected U-Net with GAN-guided training. arXiv preprint arXiv:1906.09745. 2019.
Metadata
Title
Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer
Authors
Sang Hee Ahn
Adam Unjin Yeo
Kwang Hyeon Kim
Chankyu Kim
Youngmoon Goh
Shinhaeng Cho
Se Byeong Lee
Young Kyung Lim
Haksoo Kim
Dongho Shin
Taeyoon Kim
Tae Hyun Kim
Sang Hee Youn
Eun Sang Oh
Jong Hwi Jeong
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Radiation Oncology / Issue 1/2019
Electronic ISSN: 1748-717X
DOI
https://doi.org/10.1186/s13014-019-1392-z

Other articles of this Issue 1/2019

Radiation Oncology 1/2019 Go to the issue

Review

A systematic review of dose-volume predictors and constraints for late bowel toxicity following pelvic radiotherapy

Research

Changes of lung parenchyma density following high dose radiation therapy for thoracic carcinomas – an automated analysis of follow up CT scans

Research

Risk factors of grade ≥ 2 radiation pneumonitis after gemcitabine induction chemotherapy for patients with non-small cell lung cancer

Research

Symptom palliation of hypofractionated radiotherapy for patients with incurable inflammatory breast cancer

Research

Reduction of dose to duodenum with a refined delineation method of Para-aortic region in patients with locally advanced cervical Cancer receiving prophylactic extended-field radiotherapy

Research

The urethral position may shift due to urethral catheter placement in the treatment planning for prostate radiation therapy