Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images

TL;DR

This study demonstrates that CNN-based computer-aided detection systems significantly improve the detection of small signals in 3D medical images, especially when observers explore less, highlighting the system's potential to reduce perceptual errors in volumetric imaging.

Contribution

The paper introduces a CNN-CADe system that enhances small signal detection in 3D imaging, outperforming 2D searches and reducing errors caused by limited exploration.

Findings

CNN-CADe improves 3D detection of small microcalcifications (delta AUC=0.098)

CNN-CADe enhances 2D detection of large masses (delta AUC=0.076)

Greater benefit in 3D for small signals, especially with less eye exploration

Abstract

Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3d search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors. Approach: Sixteen non-expert observers searched through digital breast tomosynthesis (DBT) phantoms and single cross-sectional slices of the DBT phantoms. The 3D/2D searches occurred with and without a convolutional neural network (CNN)-based CADe support system. The model provided observers with bounding boxes superimposed on the image stimuli while they looked for a small microcalcification signal and a large mass signal. Eye gaze positions were recorded and correlated with changes in the area under the ROC curve (AUC). Results: The CNN-CADe improved the 3D search for the small microcalcification signal (delta AUC = 0.098, p = 0.0002) and the 2D search for the large mass signal (delta AUC = 0.076, p = 0.002). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D (delta delta AUC = 0.066, p = 0.035). Analysis of individual differences suggests that those who explored the least with eye movements benefited the most from the CNN-CADe (r = -0.528, p = 0.036). However, for the large signal, the 2D benefit was not significantly greater than the 3D benefit (delta delta AUC = 0.033, p = 0.133). Conclusion: The CNN-CADe brings unique performance benefits to the 3D (vs. 2D) search of small signals by reducing errors caused by the under-exploration of the volumetric data.