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.
Footnotes
Cite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4169.
This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.
This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).
- Copyright ©the authors 2020