Effect of slab thickness on pulmonary nodule detection using maximum intensity projection in a deep learning-based computer-aided detection system

Sunyi Zheng*, Xiaonan Cui, Marleen Vonder, Raymond N.J. Veldhuis, Monique D. Dorrius, Zhaoxiang Ye, Rozemarijn Vliegenthart, Matthijs Oudkerk, Peter M.A. van Ooijen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Purpose: To investigate the effect of the slab thickness in maximum intensity projections (MIPs) by a deep learning-based computer-aided detection (DL-CAD) system on pulmonary nodule detection in CT scans

Methods and Materials: The public LIDC-IDRI dataset includes 888 CT scans with 1186 nodules annotated by four radiologists. From those scans, MIP images were reconstructed with slab thicknesses of 5 to 50 mm (at 5 mm intervals) and 3 to 13 mm (at 2 mm intervals). The proprietary DL-CAD system (MIPNOD 1.0) was trained separately using MIP images with various slab thicknesses. Based on ten-fold cross-validation, the sensitivity and the score were determined to evaluate the performance of the DL-CAD system for nodule detection.

Results: The combination of results from 16 MIP slab thickness settings showed a high sensitivity of 98.0%. The sensitivity increased (82.8% to 90.0%) for slab thickness of 1 to 10 mm and decreased (88.7% to 76.6%) for slab thickness of 15 to 50 mm. The number of false positives (FPs) was decreasing with increasing slab thickness, but was stable at 4 FP/scan at a slab thickness of 30 mm or more. With a MIP slab thickness of 10 mm, the DL-CAD system reached the highest sensitivity of 90.0%, with 8 FPs/scan.

Conclusions: Utilization of multi-MIP images could improve nodule detection of the DL-CAD system. The DL-CAD system showed the highest sensitivity for pulmonary nodule detection based on MIP images of 10 mm, similar to the slab thickness usually applied by radiologists
Original languageEnglish
Article number4169
JournalEuropean respiratory journal
Volume56
Issue number64
DOIs
Publication statusPublished - 28 Oct 2020
EventERS International Congress 2020 - Virtual
Duration: 7 Sep 20209 Sep 2020
https://erscongress.org/

Keywords

  • slab thickness
  • pulmonary nodule detection
  • deep learning
  • Computer-aided detection
  • Lung cancer

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