Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks

Overview

Journal Sensors (Basel)

Publisher MDPI

Specialty Biotechnology

Date 2020 Aug 30

PMID 32858982

Citations 1

Authors

DA-Chuan Cheng

Jen-Hong Chi

Shih-Neng Yang

Shing-Hong Liu

Affiliations

Soon will be listed here.

Abstract

In this study, we proposed a semi-automated and interactive scheme for organ contouring in radiotherapy planning for patients with non-small cell lung cancers. Several organs were contoured, including the lungs, airway, heart, spinal cord, body, and gross tumor volume (GTV). We proposed some schemes to automatically generate and vanish the seeds of the random walks (RW) algorithm. We considered 25 lung cancer patients, whose computed tomography (CT) images were obtained from the China Medical University Hospital (CMUH) in Taichung, Taiwan. The manual contours made by clinical oncologists were taken as the gold standard for comparison to evaluate the performance of our proposed method. The Dice coefficient between two contours of the same organ was computed to evaluate the similarity. The average Dice coefficients for the lungs, airway, heart, spinal cord, and body and GTV segmentation were 0.92, 0.84, 0.83, 0.73, 0.85 and 0.66, respectively. The computation time was between 2 to 4 min for a whole CT sequence segmentation. The results showed that our method has the potential to assist oncologists in the process of radiotherapy treatment in the CMUH, and hopefully in other hospitals as well, by saving a tremendous amount of time in contouring.

Citing Articles

Machine learning segmentation of core and penumbra from acute stroke CT perfusion data.

Werdiger F, Parsons M, Visser M, Levi C, Spratt N, Kleinig T Front Neurol. 2023; 14:1098562.

PMID: 36908587 PMC: 9995438. DOI: 10.3389/fneur.2023.1098562.

References

Ju W, Xiang D, Xiang D, Zhang B, Wang L, Kopriva I . Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images. IEEE Trans Image Process. 2015; 24(12):5854-67. DOI: 10.1109/TIP.2015.2488902. View

Cheng D, Chen L, Shen Y, Fuh L . Computer-assisted system on mandibular canal detection. Biomed Tech (Berl). 2016; 62(6):575-580. DOI: 10.1515/bmt-2016-0088. View

Su H, Pan H, Lu C, Chuang J, Yang T . Automatic Detection Method for Cancer Cell Nucleus Image Based on Deep-Learning Analysis and Color Layer Signature Analysis Algorithm. Sensors (Basel). 2020; 20(16). PMC: 7472205. DOI: 10.3390/s20164409. View

Chi J, Zhang S, Yu X, Wu C, Jiang Y . A Novel Pulmonary Nodule Detection Model Based on Multi-Step Cascaded Networks. Sensors (Basel). 2020; 20(15). PMC: 7435753. DOI: 10.3390/s20154301. View

Emmert-Streib F, Yang Z, Feng H, Tripathi S, Dehmer M . An Introductory Review of Deep Learning for Prediction Models With Big Data. Front Artif Intell. 2021; 3:4. PMC: 7861305. DOI: 10.3389/frai.2020.00004. View

Cheng D, Wu J, Kao Y, Su C, Liu S . Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection. J Med Syst. 2016; 40(12):260. DOI: 10.1007/s10916-016-0626-y. View

Lee S, Stewart J, Lee Y, Myrehaug S, Sahgal A, Ruschin M . Improved dosimetric accuracy with semi-automatic contour propagation of organs-at-risk in glioblastoma patients undergoing chemoradiation. J Appl Clin Med Phys. 2019; 20(12):45-53. PMC: 6909175. DOI: 10.1002/acm2.12758. View

Thariat J, Hannoun-Levi J, Myint A, Vuong T, Gerard J . Past, present, and future of radiotherapy for the benefit of patients. Nat Rev Clin Oncol. 2012; 10(1):52-60. DOI: 10.1038/nrclinonc.2012.203. View

Ghaffari M, Sanchez L, Xu G, Alaraj A, Zhou X, Charbel F . Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics. Comput Biol Med. 2018; 100:209-220. PMC: 6181589. DOI: 10.1016/j.compbiomed.2018.07.004. View

10.

Grady L . Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2006; 28(11):1768-83. DOI: 10.1109/TPAMI.2006.233. View

11.

Wang Q, Kang W, Hu H, Wang B . HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images. J Med Syst. 2016; 40(7):176. DOI: 10.1007/s10916-016-0535-0. View

12.

Stefano A, Vitabile S, Russo G, Ippolito M, Sabini M, Sardina D . An enhanced random walk algorithm for delineation of head and neck cancers in PET studies. Med Biol Eng Comput. 2016; 55(6):897-908. DOI: 10.1007/s11517-016-1571-0. View

13.

Postmus P, Kerr K, Oudkerk M, Senan S, Waller D, Vansteenkiste J . Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2017; 28(suppl_4):iv1-iv21. DOI: 10.1093/annonc/mdx222. View

14.

Uberti M, Boska M, Liu Y . A semi-automatic image segmentation method for extraction of brain volume from in vivo mouse head magnetic resonance imaging using Constraint Level Sets. J Neurosci Methods. 2009; 179(2):338-44. PMC: 2718579. DOI: 10.1016/j.jneumeth.2009.02.007. View

15.

Karamalis A, Wein W, Klein T, Navab N . Ultrasound confidence maps using random walks. Med Image Anal. 2012; 16(6):1101-12. DOI: 10.1016/j.media.2012.07.005. View

16.

Ahlawat S, Choudhary A, Nayyar A, Singh S, Yoon B . Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN). Sensors (Basel). 2020; 20(12). PMC: 7349603. DOI: 10.3390/s20123344. View

17.

Thomas H, Devakumar D, Sasidharan B, Bowen S, Heck D, Samuel E . Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization. J Med Imaging (Bellingham). 2017; 4(1):011009. PMC: 5253402. DOI: 10.1117/1.JMI.4.1.011009. View