Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Sunyi Zheng, Jiapan Guo*, Xiaonan Cui, Raymond N.J. Veldhuis, Matthijs Oudkerk, Peter M.A. Van Ooijen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

108 Citations (Scopus)
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Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.7% with 1 false positive per scan and sensitivity of 94.2% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.

Original languageEnglish
Article number8801875
Pages (from-to)797-805
Number of pages9
JournalIEEE transactions on medical imaging
Issue number3
Early online date15 Aug 2019
Publication statusPublished - Mar 2020


  • Computed tomography scans
  • Computer-aided detection (CAD)
  • Convolutional Neural Network (CNN)
  • Maximum intensity projection (MIP)
  • Pulmonary nodule detection
  • 22/2 OA procedure


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