{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:15:23Z","timestamp":1760238923477,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s20174823","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T09:05:37Z","timestamp":1598432737000},"page":"4823","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Organ Contouring for Lung Cancer Patients with a Seed Generation Scheme and Random Walks"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7955-9234","authenticated-orcid":false,"given":"Da-Chuan","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung City 40402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jen-Hong","family":"Chi","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Singapore General Hospital, Singapore 169608, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Neng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung City 40402, Taiwan"},{"name":"Department of Radiation Oncology, China Medical University Hospital, Taichung City 40447, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3923-4387","authenticated-orcid":false,"given":"Shing-Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering Chaoyang University of Technology, Taichung City 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"iv1","DOI":"10.1093\/annonc\/mdx222","article-title":"Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up","volume":"28","author":"Postmus","year":"2017","journal-title":"Ann. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1038\/nrclinonc.2012.203","article-title":"Past, present, and future of radiotherapy for the benefit of patients","volume":"10","author":"Thariat","year":"2013","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1007\/s10916-016-0535-0","article-title":"HOSVD-Based 3D Active Appearance Model: Segmentation of Lung Fields in CT Images","volume":"40","author":"Wang","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1007\/s10916-016-0626-y","article-title":"Accurate Measurement of Cross-Sectional Area of Femoral Artery on MRI Sequences of Transcontinental Ultramarathon Runners Using Optimal Parameters Selection","volume":"40","author":"Cheng","year":"2016","journal-title":"J. Med. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1515\/bmt-2016-0088","article-title":"Computer-assisted system on mandibular canal detection","volume":"62","author":"Cheng","year":"2019","journal-title":"Biomed. Eng. Biomed. Tech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"011009","DOI":"10.1117\/1.JMI.4.1.011009","article-title":"Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: Improved GrowCut algorithm with threshold initialization","volume":"4","author":"Thomas","year":"2017","journal-title":"J. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.jneumeth.2009.02.007","article-title":"A semi-automatic image segmentation method for extraction of brain volume from in vivo mouse head magnetic resonance imaging using constraint level sets","volume":"179","author":"Uberti","year":"2009","journal-title":"J. Neurosci. Methods"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1109\/TPAMI.2006.233","article-title":"Random walks for image segmentation","volume":"28","author":"Grady","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"(2020, August 01). Mathworks. Available online: https:\/\/www.mathworks.com\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5854","DOI":"10.1109\/TIP.2015.2488902","article-title":"Random Walk and Graph Cut for Co-Segmentation of Lung Tumor on PET-CT Images","volume":"24","author":"Ju","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1002\/cnm.1430","article-title":"Modified active contour model and Random Walk approach for left ventricular cardiac MR image segmentation","volume":"27","author":"Dakua","year":"2011","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1016\/j.media.2012.07.005","article-title":"Ultrasound confidence maps using random walks","volume":"16","author":"Karamalis","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s11517-016-1571-0","article-title":"An enhanced random walk algorithm for delineation of head and neck cancers in PET studies","volume":"55","author":"Stefano","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_14","unstructured":"Gonz\u00e1lez, R.C., and Woods, R.E. (2008). Digital Image Processing, Person Prentice Hall. [3rd ed.]."},{"key":"ref_15","unstructured":"Kong, T.Y., and Rosenfeld, A. (1996). Topological Algorithms for Digital Image Processing, Elsevier."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the Amount of Ecologic Association between Species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_17","first-page":"1","article-title":"A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons","volume":"5","year":"1948","journal-title":"Biol. Skar."},{"key":"ref_18","unstructured":"Krizhevsky, A. (2020, August 01). CIFAR-10 and CIFAR-100 Datasets. Available online: https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html."},{"key":"ref_19","unstructured":"(2020, August 01). The Coco Dataset. Available online: https:\/\/cocodataset.org\/#home."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., and Dehmer, M. (2020). An introductory review of deep learning for prediction models with big data. Front. Artif. Intell.","DOI":"10.3389\/frai.2020.00004"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53040","DOI":"10.1109\/ACCESS.2019.2912200","article-title":"Review of deep learning algorithms and architectures","volume":"7","author":"Shrestha","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","unstructured":"LeCun, Y., Cortes, C., and Burges, C. (2020, August 01). The MNIST Database of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_23","unstructured":"Vendt, B. (2020, August 01). LIDC-IDRI Dataset. Available online: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/LIDC-IDRI."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ahlawat, S., Choudhary, A., Nayyar, A., Singh, S., and Yoon, B. (2020). Improved handwritten digit recognition using convolutional neural networks (CNN). Sensors, 20.","DOI":"10.3390\/s20123344"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chi, J., Zhang, S., Yu, X., Wu, C., and Jiang, Y. (2020). A novel pulmonary nodule detection model based on multi-step cascaded networks. Sensors, 20.","DOI":"10.3390\/s20154301"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1002\/acm2.12758","article-title":"Improved dosimetric accuracy with semi-automatic contour propagation of organ-at-risk in glioblastoma patients","volume":"20","author":"Lee","year":"2019","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Su, H.-H., Pan, H.-W., Lu, C.-P., Chuang, J.-J., and Yang, T. (2020). Automatic detection method for cancer cell nucleus image based on deep-learning analysis and color layer signature analysis algorithm. Sensors, 20.","DOI":"10.3390\/s20164409"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0020-0190(91)90233-8","article-title":"Computing the minimum Hausdorff distance between two point sets on a line under translation","volume":"38","author":"Rote","year":"1991","journal-title":"Inf. Process. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.compbiomed.2018.07.004","article-title":"Validation of parametric mesh generation for subject-specific cerebroarterial trees using modified Hausdorff distance metrics","volume":"100","author":"Ghaffari","year":"2018","journal-title":"Comput. Biol. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4823\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:07:00Z","timestamp":1760177220000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,26]]},"references-count":29,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174823"],"URL":"https:\/\/doi.org\/10.3390\/s20174823","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,8,26]]}}}