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Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

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Abstract

Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans.

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References

  1. Adams R, Bischof L: Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16 (6): 641–647, 1994

    Google Scholar 

  2. Armato III S. G., McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, et al.: The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Med Phys 38 (2): 915–931, 2011

    PubMed  PubMed Central  Google Scholar 

  3. Badura P, Piętka E: Pre-and postprocessing stages in fuzzy connectedness-based lung nodule cad.. In: Information technologies in biomedicine. Springer, 2008, pp 192–199

  4. Badura P, Pietka E: Soft computing approach to 3d lung nodule segmentation in ct. Comput Biol Med 53: 230–243, 2014

    CAS  PubMed  Google Scholar 

  5. Bagci U, Chen X, Udupa JK: Hierarchical scale-based multiobject recognition of 3-d anatomical structures. IEEE Trans Med Imaging 31 (3): 777–789, 2011

    PubMed  Google Scholar 

  6. Béatrice P. P., Cagnazzo M, Dufaux F (2011) Motion estimation techniques. https://pdfs.semanticscholar.org/98ca/f8325abb40aa6bbddd0e7f5d3a6c366d03e6.pdf/

  7. Boykov Y, Jolly MP: Interactive organ segmentation using graph cuts.. In: International conference on medical image computing and computer-assisted intervention. Springer, 2000, pp 276–286

  8. Cavalcanti PG, Shirani S, Scharcanski J, Fong C, Meng J, Castelli J, Koff D: Lung nodule segmentation in chest computed tomography using a novel background estimation method. Quantitative Imaging in Medicine and Surgery 6 (1): 16, 2016

    PubMed  PubMed Central  Google Scholar 

  9. Cha J, Farhangi MM, Dunlap N, Amini AA: Segmentation and tracking of lung nodules via graph-cuts incorporating shape prior and motion from 4d ct. Medical Physics 45 (1): 297–306, 2018

    PubMed  Google Scholar 

  10. Choromańska A, Macura KJ: Evaluation of solitary pulmonary nodule detected during computed tomography examination. Polish Journal of Radiology 77 (2): 22, 2012

    PubMed  PubMed Central  Google Scholar 

  11. Dhara AK, Mukhopadhyay S, Khandelwal N: Computer-aided detection and analysis of pulmonary nodule from ct images: A survey. IETE Tech Rev 29 (4): 265–275, 2012

    Google Scholar 

  12. Farag AA, El Munim HEA, Graham JH, Farag AA: A novel approach for lung nodules segmentation in chest ct using level sets. IEEE Trans Image Process 22 (12): 5202–5213, 2013

    PubMed  Google Scholar 

  13. Farhangi MM, Frigui H, Seow A, Amini AA: 3-d active contour segmentation based on sparse linear combination of training shapes (scots). IEEE Trans Med Imaging 36 (11): 2239– 2249, 2017

    PubMed  Google Scholar 

  14. Farnebäck G: Two-frame motion estimation based on polynomial expansion.. In: Scandinavian conference on Image analysis. Springer, 2003, pp 363–370

  15. Firmino M, Angelo G, Morais H, Dantas MR, Valentim R: Computer-aided detection (cade) and diagnosis (cadx) system for lung cancer with likelihood of malignancy. Biomedical Engineering Online 15 (1): 2, 2016

    PubMed  PubMed Central  Google Scholar 

  16. Firmino M, Morais AH, Mendoċa RM, Dantas MR, Hekis HR, Valentim R: Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects. Biomedical Engineering Online 13 (1): 41, 2014

    PubMed  PubMed Central  Google Scholar 

  17. Galperin-Aizenberg M, Katz S, Shankla V, Wileyto E, Gefter W, Dougherty L, Torigian D, Barbosa Jr E (2020) Preliminary assessment of an optical flow method (ofm) for non-rigid registration and temporal subtraction (ts) of serial ct examinations to facilitate evaluation of interval change in metastatic lung nodules. Current Problems in Diagnostic Radiology

  18. Gong J, Liu JY, Wang L, Sun XW, Zheng B, Nie SD: Automatic detection of pulmonary nodules in ct images by incorporating 3d tensor filtering with local image feature analysis. Physica Medica 46: 124–133, 2018

    PubMed  Google Scholar 

  19. Grady L (2006) Random walks for image segmentation. IEEE Transactions on Pattern Analysis & Machine Intelligence (11):1768–1783

  20. Guo L, Zhang Y, Zhang Z, Li D, Li Y: An improved random walk segmentation on the lung nodules. International Journal of Biomathematics 6 (06): 1350043, 2013

    Google Scholar 

  21. Gupta A, Saar T, Martens O, Moullec L: Automatic detection of multi–size pulmonary nodules in ct images: large scale validation of a multi–layer perceptron based false positive reduction step. Med Phys 45 (3): 1135–1149, 2018

    PubMed  Google Scholar 

  22. 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, pp 2018

  23. Horn BK, Schunck BG: Determining optical flow. Artificial Intelligence 17 (1-3): 185–203, 1981

    Google Scholar 

  24. Hu H, Wang Q, Tang H, Xiong L, Lin Q: Multi-slice computed tomography characteristics of solitary pulmonary ground-glass nodules: Differences between malignant and benign. Thoracic Cancer 7 (1): 80–87, 2016

    PubMed  Google Scholar 

  25. Hua P, Song Q, Sonka M, Hoffman EA, Reinhardt JM: Segmentation of pathological and diseased lung tissue in ct images using a graph-search algorithm.. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2011, pp 2072–2075

  26. Javaid M, Javid M, Rehman MZU, Shah SIA: A novel approach to cad system for the detection of lung nodules in ct images. Computer Methods and Programs in Biomedicine 135: 125–139, 2016

    PubMed  Google Scholar 

  27. Khordehchi EA, Ayatollahi A, Daliri MR: Automatic lung nodule detection based on statistical region merging and support vector machines. Image Analysis & Stereology 36 (2): 65–78, 2017

    Google Scholar 

  28. Krishnamurthy S, Narasimhan G, Rengasamy U: Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives. Proceedings of the Institution of Mechanical Engineers Part H: Journal of Engineering in Medicine 230 (1): 58–70, 2016

    Google Scholar 

  29. Li B, Chen K, Peng G, Guo Y, Tian L, Ou S, Wang L: Segmentation of ground glass opacity pulmonary nodules using an integrated active contour model with wavelet energy-based adaptive local energy and posterior probability-based speed function. Materials Express 6 (4): 317–327, 2016

    CAS  Google Scholar 

  30. Liu JK, Jiang HY, He CG, Wang Y, Wang P, Ma H, et al.: An assisted diagnosis system for detection of early pulmonary nodule in computed tomography images. Journal of Medical Systems 41 (2): 30, 2017

    PubMed  Google Scholar 

  31. Lucas BD, Kanade T, et al. (1981) An iterative image registration technique with an application to stereo vision

  32. Mansoor A, Bagci U, Foster B, Xu Z, Papadakis GZ, Folio LR, Udupa JK, Mollura DJ: Segmentation and image analysis of abnormal lungs at ct: current approaches, challenges, and future trends. RadioGraphics 35 (4): 1056–1076, 2015

    PubMed  PubMed Central  Google Scholar 

  33. Mansoor A, Bagci U, Xu Z, Foster B, Olivier KN, Elinoff JM, Suffredini AF, Udupa JK, Mollura DJ: A generic approach to pathological lung segmentation. IEEE Trans Med Imaging 33 (12): 2293–2310, 2014

    PubMed  PubMed Central  Google Scholar 

  34. Mukhopadhyay S: A segmentation framework of pulmonary nodules in lung ct images. Journal of Digital Imaging 29 (1): 86–103, 2016

    PubMed  Google Scholar 

  35. Naqi SM, Sharif M, Yasmin M: Multistage segmentation model and svm-ensemble for precise lung nodule detection. Int J Comput Assist Radiol Surg 13 (7): 1083–1095, 2018

    PubMed  Google Scholar 

  36. Netto SMB, Silva AC, Nunes RA, Gattass M: Automatic segmentation of lung nodules with growing neural gas and support vector machine. Comput Biol Med 42 (11): 1110–1121, 2012

    Google Scholar 

  37. Nithila EE, Kumar S: Segmentation of lung nodule in ct data using active contour model and fuzzy c-mean clustering. Alexandria Engineering Journal 55 (3): 2583–2588, 2016

    Google Scholar 

  38. Paing MP, Hamamoto K, Tungjitkusolmun S, Visitsattapongse S, Pintavirooj C: Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest. Appl Sci 10 (7): 2346, 2020

    CAS  Google Scholar 

  39. Saien S, Moghaddam HA, Fathian M: A unified methodology based on sparse field level sets and boosting algorithms for false positives reduction in lung nodules detection. Int J Comput Assist Radiol Surg 13 (3): 397–409, 2018

    PubMed  Google Scholar 

  40. Saien S, Pilevar AH, Moghaddam HA: Refinement of lung nodule candidates based on local geometric shape analysis and laplacian of gaussian kernels. Comput Biol Med 54: 188–198, 2014

    PubMed  Google Scholar 

  41. Sellathamby D (2011) Lung segmentation data science bowl 2017. https://www.kaggle.com/dineshsellathamby/lung-segmentation/https://www.kaggle.com/dineshsellathamby/lung-segmentation/. Accessed 2019-08-10

  42. Shaukat F, Raja G, Frangi AF: Computer-aided detection of lung nodules: A review. J Med Imaging 6 (2): 020901, 2019

    Google Scholar 

  43. Shaukat F, Raja G, Gooya A, Frangi AF: Fully automatic detection of lung nodules in ct images using a hybrid feature set. Medical Phys 44 (7): 3615–3629, 2017

    Google Scholar 

  44. Sluimer I, Prokop M, Van Ginneken B: Toward automated segmentation of the pathological lung in ct. IEEE Trans Med Imaging 24 (8): 1025–1038, 2005

    Google Scholar 

  45. Suji RJ, Bhadouria SS, Dhar J, Godfrey WW: Optical flow based background subtraction method for lung nodule segmentation.. In: International conference on computer vision and image processing. Springer, 2019, pp 261–269

  46. Tajbakhsh N, Suzuki K: Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: Mtanns vs. cnns. Pattern Recognition 63: 476–486, 2017

    Google Scholar 

  47. Tan Y, Schwartz LH, Zhao B: Segmentation of lung lesions on ct scans using watershed, active contours, and Markov random field. Med Physics 40 (4): 043502, 2013

    Google Scholar 

  48. Teramoto A, Fujita H, Yamamuro O, Tamaki T: Automated detection of pulmonary nodules in pet/ct images: Ensemble false-positive reduction using a convolutional neural network technique. Medical Phys 43 (6Part1): 2821–2827, 2016

    Google Scholar 

  49. Usman M, Lee BD, Byon SS, Kim SH, IlLee B (2019) Volumetric lung nodule segmentation using adaptive roi with multi-view residual learning. arXiv:1912.13335

  50. 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, 2017

    PubMed  PubMed Central  Google Scholar 

  51. Wang YXJ, Gong JS, Suzuki K, Morcos SK: Evidence based imaging strategies for solitary pulmonary nodule. Journal of Thoracic Disease 6 (7): 872, 2014

    PubMed  PubMed Central  Google Scholar 

  52. Zhang G, Jiang S, Yang Z, Gong L, Ma X, Zhou Z, Bao C, Liu Q: Automatic nodule detection for lung cancer in ct images: A review. Comput Biol Med 103: 287–300, 2018

    PubMed  Google Scholar 

  53. Zhang J, Xia Y, Cui H, Zhang Y: Pulmonary nodule detection in medical images: a survey. Biomed ignal Process Control 43: 138–147, 2018

    Google Scholar 

  54. Zhang W, Wang X, Li X, Chen J: 3d skeletonization feature based computer-aided detection system for pulmonary nodules in ct datasets. Comput Biol Med 92: 64–72, 2018

    PubMed  Google Scholar 

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Acknowledgements

This work was carried out at ABV-IIITM Gwalior, India with funding from DST, Govt. of India. The File Number is SR/WOSA/ET-153/2017

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  1. ABV-IIITM Gwalior, ABV-IIITM Campus, Morena Link Road, Gwalior, MadhyaPradesh, 474010, India

    R. Jenkin Suji, Joydip Dhar & W. Wilfred Godfrey

  2. RGPV Bhopal, Gandhi Nagar, MadhyaPradesh, 462033, India

    Sarita Singh Bhadouria

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  1. R. Jenkin Suji
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Correspondence to R. Jenkin Suji.

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Suji, R.J., Bhadouria, S.S., Dhar, J. et al. Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images. J Digit Imaging 33, 1306–1324 (2020). https://doi.org/10.1007/s10278-020-00346-w

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