Dr. Robyn F. R. Letts Profile

Dr. Robyn F. R. Letts

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Proceedings Article | 29 May 2024 Paper

Deep learning-based mammographic breast compression pressure estimates on processed images vs an unprocessed image reference

Robyn F. Letts, Kaier Wang, Melissa Hill

Proceedings Volume 13174, 131740H (2024) https://doi.org/10.1117/12.3027039

KEYWORDS: Artificial intelligence, Breast, Digital breast tomosynthesis, Image processing, Image segmentation, Mammography, Image analysis

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Breast compression pressure (CP), computed as the force over the paddle contact area paddle, is an important measure of compression quality in mammography evidenced by associations with screening performance, including the odds of interval cancer compared to screen detected cancer. Here we introduce a novel algorithm to determine CP from processed images, the Processed Image Compression Pressure Estimator (PICPE). The aim is for PICPE outputs to align with those from an established method that estimates CP from unprocessed images such that results are comparable between image formats regardless of vendor or modality. Multiple datasets were assembled for testing of PICPE across common digital mammography (DM) and digital breast tomosynthesis (DBT) systems, representing seven different machine models from four vendors. Comparison of CP estimates derived from unprocessed and processed image pairs demonstrated excellent correlations (>0.99), with a relative difference below 5% between results from the different image formats. Uncertainties in CP estimates from variability in calibrated parameters such as the compressed breast thickness readout are expected to be substantially greater than the relative differences in estimates per image format. In future work, further testing of different image types, especially a wider variety of DBT images, should be done to confirm robust general applicability of PICPE. The results suggest that PICPE is a practical alternative algorithm for CP estimation when only processed DM or DBT images are available.

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