Accurate estimation of density and background parenchymal enhancement in breast MRI using deep regression and transformers

Grey Kuling; Belinda Curpen; Anne L . Martel

doi:10.1117/12.3025341

29 May 2024 Accurate estimation of density and background parenchymal enhancement in breast MRI using deep regression and transformers

Grey Kuling, Belinda Curpen, Anne L . Martel

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Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131740K (2024) https://doi.org/10.1117/12.3025341
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

Early detection of breast cancer is important for improving survival rates. Based on accurate and tissue-specific risk factors, such as breast density and background parenchymal enhancement (BPE), risk-stratified screening can help identify high-risk women and provide personalized screening plans, ultimately leading to better outcomes. Measurements of density and BPE are carried out through image segmentation, but volumetric measurements may not capture the qualitative scale of these tissue-specific risk factors. This study aimed to create deep regression models that estimate the interval scale underlying the BI-RADS density and BPE categories. These models incorporate a 3D convolutional encoder and transformer layers to comprehend time-sequential data in DCE-MRI. The correlation between the models and the BI-RADS categories was evaluated with Spearman coefficients. Using 1024 patients with a BI-RADS assessment score of 3 or less and no prior history of breast cancer, the models were trained on 50% of the data and tested on 50%. The density and BPE ground truth labels were extracted from the radiology reports using BI-RADS BERT. The ordinal classes were then translated to a continuous interval scale using a linear link function. The density regression model is strongly correlated to the BI-RADS category with a correlation of 0.77, slightly lower than segmentation %FGT. The BPE regression model with transformer layers shows a moderate correlation with radiologists at 0.52, similar to the segmentation %BPE. The deep regression transformer has an advantage over segmentation as it doesn’t need time-point image registration, making it easier to use.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Grey Kuling, Belinda Curpen, and Anne L . Martel "Accurate estimation of density and background parenchymal enhancement in breast MRI using deep regression and transformers", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131740K (29 May 2024); https://doi.org/10.1117/12.3025341

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KEYWORDS

Breast density

Breast

3D modeling

Data modeling

Transformers

Education and training

Image segmentation

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