Abstract
Lung cancer is the leading cause of cancer death worldwide. A lung nodule is the most common symptom of lung cancer. The analysis of lung cancer relies heavily on the segmentation of nodules, which aids in optimal treatment planning. However, because there are several lung nodules, accurate segmentation remains challenging. We propose an RW-T hybrid approach capable of segmenting all types of nodules, primarily externally attached nodules (juxta-pleural and juxta-vascular), and estimate the effect of nodule segmentation techniques to assess the quantitative Computer Tomography (CT) imaging features in lung adenocarcinoma. On 301 lung CT images from 40 patients with lung adenocarcinoma cases from the LungCT- Diagnosis dataset publicly available in The Cancer Imaging Archive, we used a random-walk strategy and a thresholding method to implement nodule segmentation (TCIA). We extracted two quantitative CT features from the segmented nodule using morphological techniques: convexity and entropy scores. The proposed method’s resultant segmented nodules are compared to the single-click ensemble segmentation method and validated using ground-truth segmented nodules. Our proposed segmentation approach had a high level of agreement with ground truth delineations, with a dice-similarity coefficient of 0.7884, compared to single-click ensemble segmentation, with a dice-similarity metric of 0.6407.
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Data availability
The dataset analysed during the current study is publicly available in the Cancer Imaging Archive (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=19039728).
References
Siegel RL, Miller KD, Fuchs HE (2022) Jemal A (2022) Cancer statistics. CA a Cancer J Clinic 72(1):7–33
Ferlay J, Ervik M, Lam F, Colombet M, Mery L, Piñeros M, Bray F (2018) Global cancer observatory: cancer today. Lyon, France: Int Agency Res Cancer 3(20):2019
Araujo LH, Horn L, Merritt RE, Shilo K, Xu-Welliver M, Carbone DP (2020) Cancer of the Lung: Non-Small Cell Lung Cancer and Small Cell Lung Cancer, Sixth, Edition. Elsevier Inc, Abeloff’s clinical oncology
Rego J, Tan KM (2006) Advances in imaging—the changing environment for the imaging specialist. Perm J 10(1):26
Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G, Miettinen OS, Smith JP (1999) Early Lung Cancer Action Project: overall design and findings from baseline screening. The Lancet 354(9173):99–105
Valente IRS, Cortez PC, Neto EC, Soares JM, de Albuquerque VHC, Tavares JMR (2016) Automatic 3D pulmonary nodule detection in CT images: a survey. Comput Methods Programs Biomed 124:91–107
Zhang G, Yang Z, Gong L, Jiang S, Wang L, Cao X, Liu Z (2019) An appraisal of nodule diagnosis for lung cancer in CT images. J Med Syst 43:1–18
Mukhopadhyay S (2016) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 29:86–103
Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 22(10):1259–1274
Xiuhua G, Tao S, Zhigang L (2011) Prediction models for malignant pulmonary nodules based-on texture features of CT image. In: Theory and Applications of CT Imaging and Analysis. IntechOpen
Hao R, Qiang Y, Yan X (2018) Juxta-vascular pulmonary nodule segmentation in PET-CT imaging based on an LBF active contour model with information entropy and joint vector. Computational and mathematical methods in medicine, 2018
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Prior F (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045–1057
Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Aerts HJ (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. Plos One 9(7):e102107
Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimisation via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239
Xiang D, Tian J, Yang F, Yang Q, Zhang X, Li Q, Liu X (2010) Skeleton cuts—An efficient segmentation method for volume rendering. IEEE Trans Visual Comput Graphics 17(9):1295–1306
Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79(1):12–49
Chen YT (2010) A level set method based on the Bayesian risk for medical image segmentation. Pattern Recogn 43(11):3699–3711
Lu K, Higgins WE (2011) Segmentation of the central-chest lymph nodes in 3D MDCT images. Comput Biol Med 41(9):780–789
Tao Y, Lu L, Dewan M, Chen AY, Corso J, Xuan J, Krishnan A (2009). Multi-level ground glass nodule detection and segmentation in CT lung images. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009: 12th International Conference, London, UK, September 20–24, 2009, Proceedings, Part II 12 (pp. 715–723). Springer Berlin Heidelberg
Wu D, Lu L, Bi J, Shinagawa Y, Boyer K, Krishnan A, Salganicoff M (2010) Stratified learning of local anatomical context for lung nodules in CT images. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2791–2798). IEEE
Dehmeshki J, Amin H, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans Med Imaging 27(4):467–480
Gu Y, Kumar V, Hall LO, Goldgof DB, Li CY, Korn R, Gillies RJ (2013) Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach. Patt Recognit 46(3):692–702
Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S, Miles K (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802
Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269(1):8–14
Nie Q, Zou YB, Lin JCW (2021) Feature extraction for medical CT images of sports tear injury. Mob Netw Appl 26:404–414
Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S, Gillies RJ (2014) Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2:1418–1426
Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, Gillies RJ (2021) Correction: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. Plos one 16(3):e0248541
Paul R, Hawkins SH, Balagurunathan Y, Schabath M, Gillies RJ, Hall LO, Goldgof DB (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2(4):388–395
Shayesteh SP, Shiri I, Karami AH, Hashemian R, Kooranifar S, Ghaznavi H, Shakeri-Zadeh A (2020) Predicting lung cancer Patients’ survival time via logistic regression-based models in a quantitative radiomic framework. J Biomed Phys Eng 10(4):479
Velazquez ER, Parmar C, Jermoumi M, Mak RH, van Baardwijk A, Fennessy FM, Aerts HJ (2013) Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep 3(1):3529
Wang S, Zhou M, Gevaert O, Tang Z, Dong D, Liu Z, Jie T (2017) A multi-view deep convolutional neural networks for lung nodule segmentation. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1752–1755). IEEE
Qi LL, Wu BT, Tang W, Zhou LN, Huang Y, Zhao SJ, Wang JW (2020) Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation. Eur Radiol 30:744–755
Li X, Li B, Liu F, Yin H, Zhou F (2020) Segmentation of pulmonary nodules using a GMM fuzzy C-means algorithm. Ieee Access 8:37541–37556
Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18 (pp. 234–241). Springer International Publishing
Funke, W., Veasey, B., Zurada, J., Frigui, H., Amini, A. (2020). 3D U-Net for segmentation of pulmonary nodules in volumetric CT scans from multi-annotator truth estimation. In: Medical Imaging 2020: Computer-Aided Diagnosis (Vol. 11314, pp. 520–527). SPIE
Xiao Z, Liu B, Geng L, Zhang F, Liu Y (2020) Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry 12(11):1787
Savic M, Ma Y, Ramponi G, Du W, Peng Y (2021) Lung nodule segmentation with a region-based fast marching method. Sensors 21(5):1908
Djenouri Y, Belhadi A, Yazidi A, Srivastava G, Chatterjee P, Lin JCW (2022). An Intelligent Collaborative Image-Sensing System for Disease Detection. IEEE Sensors J
Sun XJ, Lin JCW (2022) A target recognition algorithm of multi-source remote sensing image based on visual Internet of Things. Mob Netw Appl 27(2):784–793
Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783
Goldgof Dmitry HL, Samuel H, Matthew S, Olya S, Alberto G, Yoganand B, Robert G (2017) Long and short survival in adenocarcinoma lung CTs. Cancer Imaging Archive
Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, Gillies RJ (2015) Data from: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PloS one 10(3):e0118261
MacMahon H, Naidich DP, Goo JM, Lee KS, Leung AN, Mayo JR, Bankier AA (2017) Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284(1):228–243
Yushkevich PA, Gao Y, Gerig G (2016). ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 3342–3345). IEEE
Yushkevich PA, Pashchinskiy A, Oguz I, Mohan S, Schmitt JE, Stein JM, Gerig G (2019) User-guided segmentation of multi-modality medical imaging datasets with ITK-SNAP. Neuroinformatics 17:83–102
Adiraju RV, Elias S (2021) A survey on lung CT datasets and research trends. Res Biomed Eng 37(2):403–418
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Adiraju, R., Elias, S. A quantitative analysis of imaging features in lung CT images using the RW-T hybrid segmentation model. Multimed Tools Appl 83, 39479–39502 (2024). https://doi.org/10.1007/s11042-023-16557-6
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DOI: https://doi.org/10.1007/s11042-023-16557-6