Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Pulmonary nodule risk classification in adenocarcinoma from CT images using deep CNN with scale transfer module

  • Author(s): Jie Zheng 1 ; Dawei Yang 2, 3 ; Yu Zhu 1 ; Wanghuan Gu 1 ; Bingbing Zheng 1 ; Chunxue Bai 2, 3 ; Lin Zhao 4 ; Hongcheng Shi 5 ; Jie Hu 2, 3 ; Shaohua Lu 6 ; Weibin Shi 7 ; Ningfang Wang 2
    • View affiliations
  • Source: Volume 14, Issue 8, 19 June 2020, p. 1481 – 1489
    DOI:  10.1049/iet-ipr.2019.0248 , Print ISSN 1751-9659, Online ISSN 1751-9667
© The Institution of Engineering and Technology
Received 01/03/2019, Accepted 15/01/2020, Revised 12/12/2019, Published 28/01/2020

Pulmonary nodules risk classification in adenocarcinoma is essential for early detection of lung cancer and clinical treatment decision. Improving the level of early diagnosis and the identification of small lung adenocarcinoma has been always an important topic for imaging studies. In this study, the authors propose a deep convolutional neural network (CNN) with scale-transfer module (STM) and incorporate multi-feature fusion operation, named STM-Net. This network can amplify small targets and adapt to different resolution images. The evaluation data were obtained from the computed tomography (CT) database provided by Zhongshan Hospital Fudan University (ZSDB). All data have a pathological label and their lung adenocarcinomas risk are classified into four categories: atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, and invasive adenocarcinoma. The authors’ deep learning network STM-Net was trained and tested for the risk stage prediction. The accuracy and the average area under the receiver operating characteristic curve achieved by their method are 95.455% and 0.987 for the ZSDB dataset. The experimental results show that STM-Net largely boosts classification accuracy on the pulmonary nodules classification compared with state-of-the-art approaches. The proposed method will be an effective auxiliary to help physicians diagnosis pulmonary nodules risk classification in adenocarcinoma in early-stage.

Inspec keywords: computerised tomography; feature extraction; lung; learning (artificial intelligence); neural nets; cancer; biological organs; image classification; patient diagnosis; medical image processing

Other keywords: lung cancer; risk stage prediction; scale transfer module; deep convolutional neural network; pulmonary nodules classification; lung adenocarcinomas risk; minimally invasive adenocarcinoma; classification accuracy; computed tomography database; deep CNN; lung adenocarcinoma; imaging studies; clinical treatment decision; pulmonary nodule risk classification; early-stage; physicians diagnosis pulmonary nodules risk classification; CT images; different resolution images; authors; early diagnosis; named STM-Net; Zhongshan Hospital Fudan University

Subjects: Knowledge engineering techniques; Optical, image and video signal processing; Patient diagnostic methods and instrumentation; Other topics in statistics; Computer vision and image processing techniques; X-rays and particle beams (medical uses); Neural computing techniques; Biology and medical computing; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement)

References

    1. 1)
      • 16. Le, V., Yang, D., Zhu, Y., et al: ‘Automated classification of pulmonary nodules for lung adenocarcinomas risk evaluation: an effective CT analysis by clustering density distribution algorithm’, J. Med. Imaging. Health. Inform., 2017, 7, (8), pp. 1753–1758.
    2. 2)
      • 8. Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al: ‘Radiomics: extracting more information from medical images using advanced feature analysis’, Eur. J. Cancer, 2012, 48, (4), pp. 441–446.
    3. 3)
      • 42. Liu, Y., Hao, P., Zhang, P., et al: ‘Dense convolutional binary-tree networks for lung nodule classification’, IEEE. Access., 2018, 6, pp. 49080–49088.
    4. 4)
      • 39. Gu, W., Zhu, Y., Chen, X., et al: ‘Hierarchical CNN based real-time fatigue detection system by visual-based technologies using multi-scale pooling model’, IET Image Process., 2018, 12, (12), pp. 2319–2329.
    5. 5)
      • 44. Clay, R., Kipp, B.R., Jenkins, S., et al: ‘Computer-aided nodule assessment and risk yield (CANARY) may facilitate non-invasive prediction of EGFR mutation status in lung adenocarcinomas’, Sci. Rep., 2017, 7, (1), p. 17620.
    6. 6)
      • 37. Yan, X., Pang, J., Qi, H., et al: ‘Classification of lung nodule malignancy risk on computed tomography images using convolutional neural network: a comparison between 2D and 3D strategies’. Asian Conf. on Computer Vision, Amsterdam, The Netherlands, 2016.
    7. 7)
      • 21. Zhao, X., Liu, L., Qi, S., et al: ‘Agile convolutional neural network for pulmonary nodule classification using CT images’, Int. J. Comput. Assist. Radiol. Surg., 2018, 13, (4), pp. 585–595.
    8. 8)
      • 18. Ronneberger, O., Fischer, P., Brox, T.: ‘U-Net: convolutional networks for biomedical image segmentation’. Int. Conf. on Medical Image Computing and Computer-assisted Intervention, Munich, Germany, 2015, pp. 234–241.
    9. 9)
      • 6. Tan, Y., Schwartz, L.H., Zhao, B.: ‘Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field’, Med. Phys., 2013, 40, (4), pp. 043502–043502.
    10. 10)
      • 7. Maldonado, F., Duan, F., Raghunath, S.M., et al: ‘Noninvasive computed tomography–based risk stratification of lung adenocarcinomas in the national lung screening trial’, Am. J. Respir. Crit. Care Med., 2015, 192, (6), pp. 737–744.
    11. 11)
      • 13. Zheng, X.P., Li, M., Zhang, G.Z.: ‘Early-stage lung cancer: screening and management’ (Springer, Singapore, 2015).
    12. 12)
      • 28. Kass, M., Witkin, A., Terzopoulos, D.: ‘Snakes: active contour models’, Int. J. Comput. Vis., 1988, 1, (4), pp. 321–331.
    13. 13)
      • 32. Hojjatoleslami, S.A., Kittler, J.: ‘Region growing: a new approach’, IEEE Trans. Image Process., 1998, 7, (7), pp. 1079–1084.
    14. 14)
      • 41. Li, X.X., Li, B., Tian, L.F., et al: ‘Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm’, IET Image Process., 2018, 12, (7), pp. 1253–1264.
    15. 15)
      • 10. Li, Q., Liu, H., Su, Z.: ‘Modified fuzzy clustering with partial supervision algorithm in classification and recognition of pulmonary nodules’, J. Graphics, 2015, 36, (2), pp. 244–250.
    16. 16)
      • 14. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’, Commun. ACM, 2012, 60, (2), p. 2012.
    17. 17)
      • 38. Huang, G., Liu, Z., Maaten, L., et al: ‘Densely connected convolutional networks’. IEEE Conf. on Computer Vision and Pattern Recognition, Hawaii, HI, USA, 2017.
    18. 18)
      • 1. Dhara, A.K., Mukhopadhyay, S., Khandelwal, N.: ‘Computer-aided detection and analysis of pulmonary nodule from CT images: a survey’, IETE Tech. Rev., 2012, 29, (4), pp. 265–275.
    19. 19)
      • 27. Shi, W., Caballero, J., Huszar, F., et al: ‘Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network’. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016.
    20. 20)
      • 25. Zhao, W., Yang, J., Sun, Y., et al: ‘3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas’, Cancer Res., 2018, 78, (24), pp. 6881–6889.
    21. 21)
      • 34. Lafferty, J., Mccallum, A., Pereira, F.: ‘Conditional random fields: probabilistic models for segmenting and labeling sequence data’, Proc. ICML, 2001, 3, pp. 282–289.
    22. 22)
      • 40. Jacobs, C., Van Rikxoort, E.M., Twellmann, T., et al: ‘Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images’, Med. Image Anal., 2014, 18, (2), pp. 374–384.
    23. 23)
      • 33. Jain, A.K, Dubes, R.C.: ‘Algorithms for clustering data’, Technometrics., 1988, 32, (2), pp. 227–229.
    24. 24)
      • 22. Setio, A.A.A., Ciompi, F., Litjens, G., et al: ‘Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks’, IEEE Trans. Med. Imaging, 2016, 35, (5), pp. 1160–1169.
    25. 25)
      • 15. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016, pp. 770–778.
    26. 26)
      • 43. Rios Velazquez, E., Parmar, C., Liu, Y., et al: ‘Somatic mutations drive distinct imaging phenotypes in lung cancer’, Cancer Res., 2017, 77, (14), pp. 3922–3930.
    27. 27)
      • 17. Le, V., Yang, D., Zhu, Y., et al: ‘Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature’, Comput. Methods Programs Biomed., 2018, 160, pp. 141–151.
    28. 28)
      • 23. Shen, W., Zhou, M., Yang, F., et al: ‘Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification’, Pattern Recognit., 2017, 61, (61), pp. 663–673.
    29. 29)
      • 9. Reeves, A.P., Xie, Y., Jirapatnakul, A.: ‘Automated pulmonary nodule CT image characterization in lung cancer screening’, Int. J. Comput. Assist. Radiol. Surg., 2016, 11, (1), pp. 73–88.
    30. 30)
      • 5. Junji, S., Hiroyuki, A., Roger, E., et al: ‘Effect of the computer output on radiologists’ decision-making for classification of solitary pulmonary nodules in chest radiographs’. CARS 2002 Computer Assisted Radiology and Surgery, Paris, France, 2002, pp. 588–595.
    31. 31)
      • 36. Shore, J., Johnson, R.: ‘Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy’, IEEE Trans. Inf. Theory, 1980, 26, (1), pp. 26–37.
    32. 32)
      • 11. Alahmari, S.S., Cherezov, D., Goldgof, D.B.: ‘Delta radiomics improves pulmonary nodule malignancy prediction in lung cancer screening’, IEEE. Access., 2018, 6, pp. 77796–77806.
    33. 33)
      • 30. Osendorfer, C., Soyer, H., Smagt, P.V.D.: ‘Image super-resolution with fast approximate convolutional sparse coding’. Int. Conf. on Neural Information Processing, Montreal, Canada, 2014, pp. 250–257.
    34. 34)
      • 19. Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., et al: ‘3D U-Net: learning dense volumetric segmentation from sparse annotation’. Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece, 2016, pp. 424–432.
    35. 35)
      • 20. Öztürk, Ş., Akdemir, B.: ‘HIC-net: A deep convolutional neural network model for classification of histopathological breast images’, Comput. Electr. Eng., 2019, 76, pp. 299–310.
    36. 36)
      • 2. Jin, M.G., Chang, M.P., Hyun, J.L.: ‘Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma’, Am. J. Roentgenol., 2011, 196, (3), pp. 533–543.
    37. 37)
      • 29. Zeiler, M.D., Fergus, R.: ‘Visualizing and understanding convolutional networks’. European Conf. on Computer Vision, Zurich, Switzerland, 2014, pp. 818–833.
    38. 38)
      • 35. Wang, P., Chen, P., Yuan, Y., et al: ‘Understanding convolution for semantic segmentation’. IEEE Winter Conf. on Applications of Computer Vision, Lake Tahoe, NV, USA, 2018.
    39. 39)
      • 12. Travis, W.D., Asamura, H., Bankier, A.A., et al: ‘The IASLC lung cancer staging project: proposals for coding T categories for subsolid nodules and assessment of tumor size in part-solid tumors in the forthcoming eighth edition of the TNM classification of lung cancer travis’, J. Thorac. Oncol., 2016, 11, (8), pp. 1204–1223.
    40. 40)
      • 24. Dai, Y., Yan, S., Zheng, B., et al: ‘Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification’, Phys. Med. Biol., 2018, 63, Article number: 245004.
    41. 41)
      • 26. Zhou, P., Ni, B., Geng, C., et al: ‘Scale-transferrable object detection’. IEEE/CVF Conf. on Computer Vision and Pattern Recognition, Salt Lake City, Utah, 2018, pp. 528–537.
    42. 42)
      • 45. Oikonomou, A., Khalvati, F., Tyrrell, P.N., et al: ‘Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy’, Sci. Rep., 2018, 8, (1), p. 4003.
    43. 43)
      • 3. Kishi, K., Homma, S., Kurosaki, A., et al: ‘Small lung tumors with the size of 1 cm or less in diameter: clinical, radiological, and histopathological characteristics’, Lung Cancer, 2004, 44, (1), pp. 43–51.
    44. 44)
      • 4. Kunio, D.: ‘Computer-aided diagnosis in medical imaging: historical review, current status and future potential’, Comput. Med. Imaging Graph., 2007, 31, (4–5), pp. 198–211.
    45. 45)
      • 31. Otsu, N.: ‘A threshold selection method from gray-level histograms’, IEEE Trans. Syst. Man Cybernet., 2007, 9, (1), pp. 62–66.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2019.0248
Loading

Related content

content/journals/10.1049/iet-ipr.2019.0248
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address