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

Devi S. Klein; Srijita Karmakar; Aditya Jonnalagadda; Craig K. Abbey; Miguel P. Eckstein

doi:10.1117/1.JMI.11.4.045501

9 July 2024 Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images

Devi S. Klein, Srijita Karmakar, Aditya Jonnalagadda, Craig K. Abbey, Miguel P. Eckstein

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 4, 045501 (July 2024). https://doi.org/10.1117/1.JMI.11.4.045501

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 nonexpert 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 (Δ AUC=0.098, p=0.0002) and the 2D search for the large mass signal (Δ AUC=0.076, p=0.002). The CNN-CADe benefit in 3D for the small signal was markedly greater than in 2D (ΔΔ 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 (ΔΔ AUC=0.033, p=0.133).

Conclusion

The CNN-CADe brings unique performance benefits to the 3D (versus 2D) search of small signals by reducing errors caused by the underexploration of the volumetric data.

Citation Download Citation

Devi S. Klein, Srijita Karmakar, Aditya Jonnalagadda, Craig K. Abbey, and Miguel P. Eckstein "Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images," Journal of Medical Imaging 11(4), 045501 (9 July 2024). https://doi.org/10.1117/1.JMI.11.4.045501

Received: 16 April 2024; Accepted: 20 June 2024; Published: 9 July 2024

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KEYWORDS

Signal detection

Computer aided detection

Digital breast tomosynthesis

3D modeling

3D image processing

Eye

Visualization

Purpose

Approach

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Conclusion

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