Detection of Lung Contour with Closed Principal Curve and Machine Learning

Article

Abstract

Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization. Then the data sequences are achieved by the closed polygonal line (CPL) algorithm, where the data sequences consist of the ordered projection indexes and the corresponding initial points. Finally, the smooth lung contour can be obtained, when the data sequences are trained by the backpropagation neural network model (BNNM). We use the private clinical dataset and the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset to measure the accuracy of the presented method, respectively. To the private dataset, experimental results on the initial points which are as low as 15% of the manually delineated points show that the Dice coefficient reaches up to 0.95 and the global error is as low as 1.47 × 10−2. The performance of the proposed algorithm is also better than the cubic spline interpolation (CSI) algorithm. While on the public LIDC-IDRI dataset, our method achieves superior segmentation performance with average Dice of 0.83.

Keywords

Lung contour Principal curve Closed polygonal line algorithm Machine learning 

Notes

Acknowledgments

The authors would like to thank the Second Affiliated Hospital of Soochow University for their support.

References

  1. 1.
    Ataer-Cansizoglu E, Bas E, Kalpathy-Cramer J, Sharp GC, Erdogmus D: Contour-based shape representation using principal curves. Pattern Recognition 46:1140–1150, 2013CrossRefGoogle Scholar
  2. 2.
    Okumura E, Kawashita I, Ishida T: Computerized Classification of Pneumoconiosis on Digital Chest Radiography Artificial Neural Network with Three Stages. J Digit Imaging 30:1–14, 2017CrossRefGoogle Scholar
  3. 3.
    Wang J, Kato F, Yamashita H, Baba M, Cui Y, Li R, Oyama-Manabe N, Shirato H: Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer. J Digit Imaging 30:215–227, 2017CrossRefPubMedGoogle Scholar
  4. 4.
    Kamra A, Jain VK, Singh S, Mittal S: Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation. J Digit Imaging 29:104–114, 2016CrossRefPubMedGoogle Scholar
  5. 5.
    Song Y, Cai W, Zhou Y, Feng DD: Feature-based image patch approximation for lung tissue classification. IEEE Transactions on Medical Imaging 32:797–808, 2013CrossRefPubMedGoogle Scholar
  6. 6.
    Brosch T, Tang LYW, Yoo Y, Li DKB, Traboulsee A, Tam R: Deep 3D convolutional encoder networks with shortcuts for multiscale feature integration applied to multiple sclerosis lesion segmentation. IEEE Transactions on Medical Imaging 35:1229–1239, 2016CrossRefPubMedGoogle Scholar
  7. 7.
    Aarle WA, Batenburg KJ, Sijbers J: Optimal threshold selection for segmentation of dense homogeneous objects in tomographic reconstructions. IEEE Transactions on Medical Imaging 30:980–989, 2011CrossRefPubMedGoogle Scholar
  8. 8.
    Zhao Y, Rada L, Chen K, Harding SP, Zheng Y: Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images. IEEE Transactions on Medical Imaging 34:1797–1807, 2015CrossRefPubMedGoogle Scholar
  9. 9.
    Barbu A, Suehling M, Xu X, Liu D, Zhou SK, Comaniciu D: Automatic detection and segmentation of lymph nodes from CT data. IEEE Transactions on Medical Imaging 31:240–250, 2012CrossRefPubMedGoogle Scholar
  10. 10.
    Bates R, Irving B, Markelc B, Kaeppler J, Brown G, Muschel RJ, Brady M, Grau V, Schnabel JA: Segmentation of Vasculature from Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images. IEEE Transactions on Medical Imaging 99:1–10, 2017CrossRefGoogle Scholar
  11. 11.
    Ali S, Madabhushi A: An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Transactions on Medical Imaging 31:1448–1460, 2012CrossRefPubMedGoogle Scholar
  12. 12.
    Tang L, Niemeijer M, Reinhardt JM, Garvin MK, Abràmoff MD: Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Transactions on Medical Imaging 32:364–375, 2013CrossRefPubMedGoogle Scholar
  13. 13.
    Vogl WD, Waldstein SM, Gerendas BS, Schmidt-Erfurth U, Langs G: Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images. IEEE Transactions on Medical Imaging 36:1773–1783, 2017CrossRefPubMedGoogle Scholar
  14. 14.
    Maggio S, Palladini A, Marchi LD, Alessandrini M, Speciale N, Masetti G: Predictive deconvolution and hybrid feature selection for computer-aided detection of prostate cancer. IEEE Transactions on Medical Imaging 29:455–464, 2010CrossRefPubMedGoogle Scholar
  15. 15.
    Pu J, Fuhrman C, Good WF, Sciurba FC, Gur D: A differential geometric approach to automated segmentation of human airway tree. IEEE Transactions on Medical Imaging 30:266–278, 2011CrossRefPubMedGoogle Scholar
  16. 16.
    Dai S, Lu K, Dong J, Zhang Y, Chen Y: A novel approach of lung segmentation on chest CT images using graph cuts. Neurocomputing 168:799–807, 2015CrossRefGoogle Scholar
  17. 17.
    Zhou H, Goldgof DB, Hawkins S, Wei L, Liu Y, Creighton D, Gillies RJ, Hall LO, Nahavandi S: A robust approach for automated lung segmentation in thoracic CT. Proceedings of the 2015 I.E. conference on Systems, Man, and Cybernetics (SMC), Kowloon, China, 2015, pp 2267–2272Google Scholar
  18. 18.
    Tu L, Styner M, Vicory J, Elhabian S, Wang R, Hong J, Paniagua B, Prieto JC, Yang D, Whitaker R, Pizer SM: Skeletal Shape Correspondence through Entropy. IEEE Transactions on Medical Imaging 99:1–10, 2017Google Scholar
  19. 19.
    Shao Y, Gao Y, Guo Y, Shi Y, Yang X, Shen D: Hierarchical lung field segmentation with joint shape and appearance sparse learning. IEEE Transactions on Medical Imaging 33:1761–1780, 2014CrossRefPubMedGoogle Scholar
  20. 20.
    Soliman A, Khalifa F, Elnakib A, El-Ghar MA, Dunlap N, Wang B, Gimel’farb G, Keynton R, El-Baz A: Accurate lungs segmentation on CT chest images by adaptive appearance-guided shape modeling. IEEE Transactions on Medical Imaging 36:263–276, 2017CrossRefPubMedGoogle Scholar
  21. 21.
    Dhara AK, Mukhopadhyay S, Dutta A, Garg M, Khandelwal N: A combination of shape and texture features for classification of pulmonary nodules in lung ct images. J Digit Imaging 29:466–475, 2016CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Nguyen HV, Porikli F: Support vector shape: A classifier-based shape representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 35:970–982, 2013CrossRefPubMedGoogle Scholar
  23. 23.
    Deng C, Lin H: Progressive and iterative approximation for least squares B-spline curve and surface fitting. Computer-Aided Design 47:32–44, 2014CrossRefGoogle Scholar
  24. 24.
    Xiao C, Staring M, Shamonin D, Reiber JHC, Stolk J, Stoel BC: A strain energy filter for 3D vessel enhancement with application to pulmonary CT images. Medical Image Analysis 15:112–124, 2011CrossRefPubMedGoogle Scholar
  25. 25.
    Shepherd T, Prince SJD, Alexander DC: Interactive lesion segmentation with shape priors from offline and online learning. IEEE Transactions on Medical Imaging 31:1698–1712, 2012CrossRefPubMedGoogle Scholar
  26. 26.
    Song Q, Bai J, Garvin MK, Sonka M, Buatti JM, Wu X: Optimal multiple surface segmentation with shape and context priors. IEEE Transactions on Medical Imaging 32:376–386, 2013CrossRefPubMedGoogle Scholar
  27. 27.
    Zhang S, Zhan Y, Metaxas DN: Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis 16:1385–1396, 2012CrossRefPubMedGoogle Scholar
  28. 28.
    Heibel H, Glocker B, Groher M, Pfister M, Navab N: Interventional tool tracking using discrete optimization. IEEE Transactions on Medical Imaging 32:544–555, 2013CrossRefPubMedGoogle Scholar
  29. 29.
    Aquino A, Gegúndez-Arias ME, Marín D: Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Transactions on Medical Imaging 29:1860–1869, 2010CrossRefPubMedGoogle Scholar
  30. 30.
    Li Z, Zhang Y, Liu G, Shao H, Li W, Tang X: A robust coronary artery identification and centerline extraction method in angiographies. Biomedical Signal Processing and Control 16:1–8, 2015CrossRefGoogle Scholar
  31. 31.
    Hastie T, Stuetzle W: Principal curves. Journal of the American Statistical Association 84:502–516, 1989CrossRefGoogle Scholar
  32. 32.
    Bradley RS, Withers PJ: Post-processing techniques for making reliable measurements from curve-skeletons. Computers in biology and medicine 72:120–131, 2016CrossRefPubMedGoogle Scholar
  33. 33.
    Kégl B, Krzyzak A: Piecewise linear skeletonization using principal curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:59–74, 2002CrossRefGoogle Scholar
  34. 34.
    Yu Y, Wang J: Enclosure Transform for Interest Point Detection From Speckle Imagery. IEEE Transactions on Medical Imaging 36:769–780, 2017CrossRefGoogle Scholar
  35. 35.
    Khedher L, Ramírez J, Górriz JM, Brahim A, Segovia F: Early diagnosis of Alzheimer’ s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images. Neurocomputing 151:139–150, 2015CrossRefGoogle Scholar
  36. 36.
    Zhu X, Ge Y, Li T, Thongphiew D, Yin FF, Wu QJ: A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. Medical physics 38:719–726, 2011CrossRefPubMedGoogle Scholar
  37. 37.
    Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall A: G: Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach. Medical Physics 44:5210–5220, 2017CrossRefPubMedGoogle Scholar
  38. 38.
    Tseng HH, Luo Y, Cui S, Chien JT, Ten Haken RK, Naqa IE: Deep reinforcement learning for automated radiation adaptation in lung cancer. Medical Physics 44:6690–6705, 2017CrossRefPubMedGoogle Scholar
  39. 39.
    Ma J, Wu F, Jiang TA, Zhu J, Kong D: Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Medical Physics 44:1678–1691, 2017CrossRefPubMedGoogle Scholar
  40. 40.
    Shaukat F, Raja G, Gooya A, Frangi AF: Fully automatic detection of lung nodules in CT images using a hybrid feature set. Medical Physics 44:3615–3629, 2017CrossRefPubMedGoogle Scholar
  41. 41.
    Taigman Y, Yang M, Ranzato MA, Wolf L: Deepface: Closing the gap to human-level performance in face verification. Proceedings of the 27th IEEE conference on computer vision and pattern recognition, Columbus, USA, 2014, pp 1701–1708Google Scholar
  42. 42.
    Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E: Deep learning applications and challenges in big data analytics. Journal of Big Data 2:1, 2015CrossRefGoogle Scholar
  43. 43.
    Kégl B, Krzyzak A, Linder T, Zeger K: Learning and design of principal curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 22:281–297, 2000CrossRefGoogle Scholar
  44. 44.
    Armato SG, McLennan G, Bidaut L et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Medical Physics 38:915–931, 2011CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R: Dropout: a simple way to prevent neural networks from overfitting. Journal of machine learning research 15:1929–1958, 2014Google Scholar
  46. 46.
    LeCun Y, Bengio Y, Hinton GE: Deep learning. Nature 521:436–444, 2015CrossRefPubMedGoogle Scholar
  47. 47.
    Wang S, Zhou M, Gevaert O, Tang Z, Dong D, Liu Z, Tian J: A multi-view deep convolutional neural networks for lung nodule segmentation. Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, South Korea, 2017, pp 1752–1755Google Scholar
  48. 48.
    Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, Dong D, Gevaert O, Tian J: Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Medical image analysis 40:172–183, 2017CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Song J, Yang C, Fan L, Wang K, Yang F, Liu S, Tian J: Lung lesion extraction using a toboggan based growing automatic segmentation approach. IEEE transactions on medical imaging 35:337–353, 2016CrossRefPubMedGoogle Scholar
  50. 50.
    Kubota T, Jerebko AK, Dewan M, Salganicoff M, Krishnan A: Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Medical Image Analysis 15:133–154, 2011CrossRefPubMedGoogle Scholar

Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.School of Computer Science & TechnologySoochow UniversitySuzhouChina

Personalised recommendations