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Comparison of Conventional and Deep Learning Based Methods for Pulmonary Nodule Segmentation in CT Images

  • Joana RochaEmail author
  • António Cunha
  • Ana Maria Mendonça
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11804)

Abstract

Lung cancer is among the deadliest diseases in the world. The detection and characterization of pulmonary nodules are crucial for an accurate diagnosis, which is of vital importance to increase the patients’ survival rates. The segmentation process contributes to the mentioned characterization, but faces several challenges, due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper proposes two methods for pulmonary nodule segmentation in Computed Tomography (CT) scans. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The second approach is based on Deep Learning, using the U-Net to achieve the same goal. This work compares both performances, and consequently identifies which one is the most promising tool to promote early lung cancer screening and improve nodule characterization. Both methodologies used 2653 nodules from the LIDC database: the SBF based one achieved a Dice score of 0.663, while the U-Net achieved 0.830, yielding more similar results to the ground truth reference annotated by specialists, and thus being a more reliable approach.

Keywords

Computer-aided diagnosis Conventional Deep Learning Lung Nodule Segmentation Sliding Band Filter U-Net 

Notes

Acknowledgements

This work is financed by National Funds through the Portuguese funding agency, FCT – Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Joana Rocha
    • 1
    • 2
    Email author
  • António Cunha
    • 2
    • 3
  • Ana Maria Mendonça
    • 1
    • 2
  1. 1.Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  2. 2.INESC TEC – INESC Technology and SciencePortoPortugal
  3. 3.Universidade de Trás-os-Montes e Alto DouroVila RealPortugal

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