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An efficient U-Net framework for lung nodule detection using densely connected dilated convolutions

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Abstract

Remote health monitoring is an important aspect especially for remote locations where standard medical facilities are not available. Smart cities use a similar concept to provide health facilities even when physicians are unavailable. Lung cancer remains to be one of the most critical types of cancer with a 5-year survival rate of only 18%. Efficient computer-aided diagnostic systems are required to diagnose lung cancer before time for better treatment planning. The variety of lung nodules and their visual similarity with surrounding regions make their detection difficult. Traditional image processing and machine learning methods usually lack the ability to handle all types of nodules with a single method. In this study, we propose an efficient end-to-end segmentation algorithm with an improved feature learning mechanism based on densely connected dilated convolutions. We applied dense feature extraction and incorporated multi-dilated context learning by using dilated convolutions at different rates for better nodule segmentation. First, lung ROIs are extracted from the CT scans using k-mean clustering and morphological operators to reduce the model’s search space instead of using full CT scan images or nodule patches. These ROIs are then used by our proposed architecture for nodule segmentation and efficiently handles different types of lung nodules. The performance of the proposed algorithm is evaluated on a publicly available dataset LIDC-IDRI and achieved a dice score of 81.1% and a Jaccard score of 72.5%.

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References

  1. Siegel RL, Miller KD, Jemal A (2016) Cancer statistics. CA Cancer J Clin 66(1):7–30

    Article  Google Scholar 

  2. Thomas GAS, Robinson YH, Julie EG, Shanmuganathan V, Nam Y, Rho S (2020) Diabetic retinopathy diagnostics from retinal images based on deep convolutional networks.

  3. Nawaz H, Maqsood M, Afzal S, Aadil F, Mehmood I, Rho S (2020) A deep feature-based real-time system for Alzheimer disease stage detection. Multimedia Tools and Applications:1–19

  4. Jung S, Moon J, Park S, Rho S, Baik SW, Hwang E (2020) Bagging ensemble of multilayer perceptrons for missing electricity consumption data imputation. Sensors 20(6):1772

    Article  Google Scholar 

  5. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5(1):1–9

    Google Scholar 

  6. Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ (2006) On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 25(4):435–450

    Article  Google Scholar 

  7. Lassen B, Jacobs C, Kuhnigk J, Van Ginneken B, Van Rikxoort E (2015) Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans. Phys Med Biol 60(3):1307

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  9. Kubota T, Jerebko AK, Dewan M, Salganicoff M, Krishnan A (2011) Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models. Med Image Anal 15(1):133–154

    Article  Google Scholar 

  10. Jj Z, Ji Gh, Xia Y, Xl Z (2015) Cavitary nodule segmentation in computed tomography images based on self–generating neural networks and particle swarm optimisation. Int J Bio-Inspir Comput 7(1):62–67

    Article  Google Scholar 

  11. Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M (2011) Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. IEEE Trans Biomed Eng 58(12):3418–3428

    Article  Google Scholar 

  12. Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406

    Article  Google Scholar 

  13. Ye X, Beddoe G, Slabaugh G (2010) Automatic graph cut segmentation of lesions in CT using mean shift superpixels. Int J Biomed Imaging. https://doi.org/10.1155/2010/983963

    Article  Google Scholar 

  14. Messay T, Hardie RC, Tuinstra TR (2015) Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Med Image Anal 22(1):48–62

    Article  Google Scholar 

  15. Keshani M, Azimifar Z, Tajeripour F, Boostani R (2013) Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system. Comput Biol Med 43(4):287–300

    Article  Google Scholar 

  16. Ding J, Li A, Hu Z, Wang L Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017. Springer, pp 559-567

  17. Pereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 35(5):1240–1251

    Article  Google Scholar 

  18. Robinson YH, Vimal S, Julie EG, Narayanan KL, Rho S (2021) 3-Dimensional Manifold and Machine Learning Based Localization Algorithm for Wireless Sensor Networks. Wireless Personal Communications:1–19

  19. Jifara W, Jiang F, Rho S, Cheng M, Liu S (2019) Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput 75(2):704–718

    Article  Google Scholar 

  20. Kalsoom A, Maqsood M, Ghazanfar MA, Aadil F, Rho S (2018) A dimensionality reduction-based efficient software fault prediction using Fisher linear discriminant analysis (FLDA). J Supercomput 74(9):4568–4602

    Article  Google Scholar 

  21. Jiang F, Grigorev A, Rho S, Tian Z, Fu Y, Jifara W, Adil K, Liu S (2018) Medical image semantic segmentation based on deep learning. Neural Comput Appl 29(5):1257–1265

    Article  Google Scholar 

  22. Valverde S, Oliver A, Roura E, González-Villà S, Pareto D, Vilanova JC, Ramió-Torrentà L, Rovira À, Lladó X (2017) Automated tissue segmentation of MR brain images in the presence of white matter lesions. Med Image Anal 35:446–457

    Article  Google Scholar 

  23. Moeskops P, Viergever MA, Mendrik AM, De Vries LS, Benders MJ, Išgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5):1252–1261

    Article  Google Scholar 

  24. Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108:214–224

    Article  Google Scholar 

  25. Pezzano G, Ripoll VR, Radeva P (2021) CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Comput Methods Programs Biomed 198:105792

    Article  Google Scholar 

  26. Keetha NV, Annavarapu CSR (2020) U-Det: A modified U-Net architecture with bidirectional feature network for lung nodule segmentation. arXiv preprint

  27. Ronneberger O, Fischer P, Brox T U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, 2015. Springer, pp 234-241

  28. 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

    Article  Google Scholar 

  29. Sargent D, Park SY Semi-automatic 3D lung nodule segmentation in CT using dynamic programming. In: Medical Imaging 2017: Image Processing, 2017. International Society for Optics and Photonics, p 101332R

  30. Kuhnigk J-M, Dicken V, Bornemann L, Bakai A, Wormanns D, Krass S, Peitgen H-O (2006) Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 25(4):417–434

    Article  Google Scholar 

  31. Wang J, Guo H (2016) Automatic approach for lung segmentation with juxta-pleural nodules from thoracic CT based on contour tracing and correction. Comput Math Methods Med. https://doi.org/10.1155/2016/2962047

    Article  MATH  Google Scholar 

  32. Nithila EE, Kumar S (2016) Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering. Alex Eng J 55(3):2583–2588

    Article  Google Scholar 

  33. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  Google Scholar 

  34. Mukherjee S, Huang X, Bhagalia RR Lung nodule segmentation using deep learned prior based graph cut. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), 2017. IEEE, pp 1205–1208

  35. Mukhopadhyay S (2016) A segmentation framework of pulmonary nodules in lung CT images. J Digit Imaging 29(1):86–103

    Article  Google Scholar 

  36. Shen S, Bui AA, Cong J, Hsu W (2015) An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med 57:139–149

    Article  Google Scholar 

  37. Lu L, Devarakota P, Vikal S, Wu D, Zheng Y, Wolf M Computer aided diagnosis using multilevel image features on large-scale evaluation. In: International MICCAI Workshop on Medical Computer Vision, 2013. Springer, pp 161–174

  38. Hu Y, Menon PG A neural network approach to lung nodule segmentation. In: Medical Imaging 2016: Image Processing, 2016. International Society for Optics and Photonics, p 97842O

  39. Jung J, Hong H, Goo JM (2018) Ground-glass nodule segmentation in chest CT images using asymmetric multi-phase deformable model and pulmonary vessel removal. Comput Biol Med 92:128–138

    Article  Google Scholar 

  40. Gonçalves L, Novo J, Campilho A (2016) Hessian based approaches for 3D lung nodule segmentation. Expert Syst Appl 61:1–15

    Article  Google Scholar 

  41. Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, Dong D, Gevaert O, Tian J (2017) Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172–183

    Article  Google Scholar 

  42. Huang X, Sun W, Tseng T-LB, Li C, Qian W (2019) Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput Med Imaging Graph 74:25–36

    Article  Google Scholar 

  43. Cao H, Liu H, Song E, Hung C-C, Ma G, Xu X, Jin R, Lu J (2020) Dual-branch residual network for lung nodule segmentation. Appl Soft Comput 86:105934

    Article  Google Scholar 

  44. Ali I, Muzammil M, Haq IU, Khaliq AA, Abdullah S (2020) efficient lung nodule classification using transferable texture convolutional neural network. IEEE Access 8:175859–175870

    Article  Google Scholar 

  45. Jiang J, Hu Y-C, Liu C-J, Halpenny D, Hellmann MD, Deasy JO, Mageras G, Veeraraghavan H (2018) Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images. IEEE Trans Med Imaging 38(1):134–144

    Article  Google Scholar 

  46. Liu M, Dong J, Dong X, Yu H, Qi L Segmentation of lung nodule in CT images based on mask R-CNN. In: 2018 9th International Conference on Awareness Science and Technology (iCAST), 2018. IEEE, pp 1–6

  47. Tang H, Zhang C, Xie X Nodulenet: Decoupled false positive reduction for pulmonary nodule detection and segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019. Springer, pp 266-274

  48. Ioffe S, Szegedy C Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, 2015. PMLR, pp 448–456

  49. Nair V, Hinton GE Rectified linear units improve restricted boltzmann machines. In: Icml, 2010.

  50. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv preprint

  51. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017. pp 4700–4708

  52. He K, Zhang X, Ren S, Sun J Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision, 2015. pp 1026–1034

  53. Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA (2011) 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

    Article  Google Scholar 

  54. Bukhari M, Bajwa KB, Gillani S, Maqsood M, Durrani MY, Mehmood I, Ugail H, Rho S (2020) An efficient gait recognition method for known and unknown covariate conditions. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3047266

    Article  Google Scholar 

  55. Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y, Tian J (2017) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recogn 61:663–673

    Article  Google Scholar 

  56. Huang X, Shan J, Vaidya V Lung nodule detection in CT using 3D convolutional neural networks. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017. IEEE, pp 379–383

  57. Wu B, Zhou Z, Wang J, Wang Y Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018. IEEE, pp 1109–1113

  58. Hancock MC, Magnan JF (2019) Lung nodule segmentation via level set machine learning. arXiv preprint

  59. Zhao X, Sun W, Qian W, Qi S, Sun J, Zhang B, Yang Z Fine-grained lung nodule segmentation with pyramid deconvolutional neural network. In: Medical Imaging 2019: Computer-Aided Diagnosis, 2019. International Society for Optics and Photonics, p 109503S

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Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan

    Zeeshan Ali & Aun Irtaza

  2. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Islamabad, Pakistan

    Muazzam Maqsood

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  1. Zeeshan Ali
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  2. Aun Irtaza
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Ali, Z., Irtaza, A. & Maqsood, M. An efficient U-Net framework for lung nodule detection using densely connected dilated convolutions. J Supercomput 78, 1602–1623 (2022). https://doi.org/10.1007/s11227-021-03845-x

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