Adaptive thresholding technique for segmenting breast dense tissue in digital breast tomosynthesis: a preliminary study

Tamerlan Mustafaev; Robert M. Nishikawa; Juhun Lee

doi:10.1117/12.3027017

29 May 2024 Adaptive thresholding technique for segmenting breast dense tissue in digital breast tomosynthesis: a preliminary study

Tamerlan Mustafaev, Robert M. Nishikawa, Juhun Lee

Author Affiliations +

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 1317411 (2024) https://doi.org/10.1117/12.3027017
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

Digital Breast Tomosynthesis (DBT) is an imaging modality with improved breast tissue characterization, which is crucial for early cancer detection. Yet, research on dense tissue segmentation in DBT is scarce, challenged by the complexity of multi-slice variation and blurring and out-of-plane artifacts. This study introduces and validates a semi-automatic approach for breast density segmentation in DBT, aiming to enhance dataset creation and improve deep learning models for accurate breast density segmentation and volume estimation. Our semi-automated method begins with a radiologist annotating the central slice of the DBT series using a polygon mask, accompanied by the selection of a threshold value to accurately segment dense tissue portions. This initial annotation serves as a reference for extending the mask segmentation to all slices in the series, with threshold values iteratively adjusted for each slice to ensure precise and consistent segmentation. We analyzed the DBT series from 100 patients (13,094 slices), validating our approach against an independent expert radiologist’s assessments through Pearson’s correlation. For comparison, we evaluated a fixed threshold technique, which applies a manually selected threshold from the central slice to all slices in the DBT series, and a 2D CNN algorithm that was trained on 2D mammograms. Our semi-automated method showed the highest correlation (0.855-0.858, CI 0.813–0.89), surpassing the 2D CNN (0.617-0.645, CI 0.524-0.719) and fixed threshold (0.506-0.794, CI 0.39-0.84) techniques.

Citation Download Citation

Tamerlan Mustafaev, Robert M. Nishikawa, and Juhun Lee "Adaptive thresholding technique for segmenting breast dense tissue in digital breast tomosynthesis: a preliminary study", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 1317411 (29 May 2024); https://doi.org/10.1117/12.3027017

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