Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images


Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients’ survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter’s support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task.

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  1. 1.

    Badrinarayanan V., Kendall A., Cipolla R. (2015) SegNet: A deep convolutional encoder-decoder, architecture for image segmentation. arXiv:1511.00561 [cs]

  2. 2.

    Dashtbozorg B., Mendonça A.M., Campilho A. (2015) Optic disc segmentation using the sliding band filter, vol 56.

  3. 3.

    Do Nhu T., Joo S.D., Yang H.J., Taek Jung S., Kim S. (2019) Knee Bone Tumor Segmentation from radiographs using Seg-Unet with Dice Loss

  4. 4.

    Dubois D., Hájek P., Prade H. (2000) Knowledge-driven versus data-driven logics. Journal of logic, Language and Information, pp. 65–89.

  5. 5.

    van Ginneken B.: Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol. Phys. Technol. 10 (1): 23–32, 2017

    Article  Google Scholar 

  6. 6.

    Kingma D.P., Ba J. (2014) Adam: A method for stochastic optimization. arXiv:1412.6980

  7. 7.

    Kumar P., Nagar P., Arora C., Gupta A. (2018) U-SegNet,: Fully Convolutional Neural Network based Automated Brain tissue segmentation Tool. arXiv:1806.04429 [cs]

  8. 8.

    LeCun Y., Bengio Y., Hinton G.: Deep learning. Nature 521 (7553): 436–444, 2015.

    CAS  Article  Google Scholar 

  9. 9.

    Markel D., Caldwell C., Alasti H., Soliman H., Ung Y., Lee J., Sun A. (2013) Automatic segmentation of lung carcinoma using 3d texture features in 18-fdg pet/ct. International journal of molecular imaging 2013

  10. 10.

    Quelhas P., Marcuzzo M., Mendonca A.M., Campilho A.: Cell nuclei and cytoplasm joint segmentation using the sliding band filter. IEEE Trans. Med. Imaging 29 (8): 1463–1473, 2010.

    Article  Google Scholar 

  11. 11.

    Ronneberger O., Fischer P., Brox T. (2015) U-Net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention. pp. 234–241. Springer

  12. 12.

    Roth H.R., Shen C., Oda H., Oda M., Hayashi Y., Misawa K., Mori K. (2018) Deep learning and its application to medical image segmentation. arXiv:1803.08691 [cs]

  13. 13.

    Shakibapour E., Cunha A., Aresta G., Mendonça A.M., Campilho A.: An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung ct scans. Expert Syst. Appl. 119: 415–428, 2019

    Article  Google Scholar 

  14. 14.

    Torre L.A., Siegel R.L., Jemal A.: Lung cancer statistics. In: (Ahmad A., Gadgeel S., Eds.) Lung Cancer and Personalized Medicine, vol 893. Springer International Publishing, Cham, 2016, pp 1–19.

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This study was funded by the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019.

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Correspondence to Joana Rocha.

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Joana Rocha declares that she has no conflict of interest. António Cunha declares that he has no conflict of interest. Ana Maria Mendonça declares that she has no conflict of interest.

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Rocha, J., Cunha, A. & Mendonça, A.M. Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images. J Med Syst 44, 81 (2020).

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  • Computer-aided diagnosis
  • Conventional
  • Deep learning
  • Image analysis
  • Lung
  • Nodule
  • Segmentation