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
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 language | English |
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Article number | 4169 |
Journal | European respiratory journal |
Volume | 56 |
Issue number | 64 |
DOIs | |
Publication status | Published - 28 Oct 2020 |
Event | ERS International Congress 2020 - Virtual Duration: 7 Sep 2020 → 9 Sep 2020 https://erscongress.org/ |
Keywords
- slab thickness
- pulmonary nodule detection
- deep learning
- Computer-aided detection
- Lung cancer