AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net

Ahmad Qasem; Genggeng Qin; Zhiguo Zhou

doi:10.1117/1.JMI.11.2.024005

23 March 2024 AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net

Ahmad Qasem, Genggeng Qin, Zhiguo Zhou

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 2, 024005 (March 2024). https://doi.org/10.1117/1.JMI.11.2.024005

Abstract

Purpose

The objective of this study was to develop a fully automatic mass segmentation method called AMS-U-Net for digital breast tomosynthesis (DBT), a popular breast cancer screening imaging modality. The aim was to address the challenges posed by the increasing number of slices in DBT, which leads to higher mass contouring workload and decreased treatment efficiency.

Approach

The study used 50 slices from different DBT volumes for evaluation. The AMS-U-Net approach consisted of four stages: image pre-processing, AMS-U-Net training, image segmentation, and post-processing. The model performance was evaluated by calculating the true positive ratio (TPR), false positive ratio (FPR), F-score, intersection over union (IoU), and 95% Hausdorff distance (pixels) as they are appropriate for datasets with class imbalance.

Results

The model achieved 0.911, 0.003, 0.911, 0.900, 5.82 for TPR, FPR, F-score, IoU, and 95% Hausdorff distance, respectively.

Conclusions

The AMS-U-Net model demonstrated impressive visual and quantitative results, achieving high accuracy in mass segmentation without the need for human interaction. This capability has the potential to significantly increase clinical efficiency and workflow in DBT for breast cancer screening.

Citation Download Citation

Ahmad Qasem, Genggeng Qin, and Zhiguo Zhou "AMS-U-Net: automatic mass segmentation in digital breast tomosynthesis via U-Net," Journal of Medical Imaging 11(2), 024005 (23 March 2024). https://doi.org/10.1117/1.JMI.11.2.024005

Received: 12 August 2023; Accepted: 11 March 2024; Published: 23 March 2024

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KEYWORDS

Image segmentation

Digital breast tomosynthesis

Gaussian filters

Education and training

Image processing

Performance modeling

Breast cancer

Purpose

Approach

Results

Conclusions

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