P42.06 Automatic Lung Nodule Detection by a Deep Learning-Based CAD System: The Value of Slab Thickness in the Maximum Intensity Projection Technique

Abstract

Deep learning techniques have shown good results on automated lung nodule detection. The maximum intensity projection technique (MIP) has been proved that it can improve the performance of nodule detection in the deep learning-based computer-aided detection (DL-CAD) system. But the optimal slab thickness is unknown. To provide better assistance for radiologists, the aim of this study is to investigate the effect of the slab thickness in maximum intensity projections by a DL-CAD system on pulmonary nodule detection in CT scans. 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 DL-CAD system was trained separately using MIP images with various slab thicknesses. Based on ten-fold cross-validation, the sensitivity and the F2 score were determined to evaluate the performance of the DL-CAD system for nodule detection. 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. The utilization of multi-MIP images could further improve the performance of lung nodule detection in 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.