Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation

Scientific Reports, vol. 13, pp. 14207

Abstract

Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909 ± 0.069 versus 0.916 ± 0.067, P < 0.001) and on the external testset (0.824 ± 0.144 versus 0.864 ± 0.081, P = 0.004). Moreover, the average symmetric surface distance was higher (= worse) for nnUNet than for TraBS on the internal (0.657 ± 2.856 versus 0.548 ± 2.195, P = 0.001) and on the external testset (0.727 ± 0.620 versus 0.584 ± 0.413, P = 0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening.

Authors 12

1. Gustav Anton Müller‐Franzes Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

2. Fritz Müller-Franzes Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

3. Luisa Charlotte Huck Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

4. Vanessa Raaff Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

5. Eva Kemmer Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

6. Firas Khader Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

7. Soroosh Tayebi Arasteh Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

8. Teresa Lemainque Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

9. Jakob Nikolas Kather Aachen Department of Medicine III

   Universitätsklinikum Aachen · Else Kröner Fresenius Center for Digital Health · RWTH Aachen University

   Affiliation as printed

   Department of Medicine III, University Hospital RWTH, Aachen, Germany

   Else Kroener Fresenius Center for Digital Health, Technical University, Dresden, Germany

10. Sven Nebelung Aachen Department of Diagnostic and Interventional Radiology

    Universitätsklinikum Aachen · RWTH Aachen University

    Affiliation as printed

    Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

11. Christiane Katharina Kuhl Aachen Department of Diagnostic and Interventional Radiology

    Universitätsklinikum Aachen · RWTH Aachen University

    Affiliation as printed

    Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany

12. Daniel Truhn corresponding Aachen Department of Diagnostic and Interventional Radiology

    Universitätsklinikum Aachen · RWTH Aachen University

    Affiliation as printed

    Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany. dtruhn@ukaachen.de

    Department of Diagnostic and Interventional Radiology, University Hospital RWTH, Aachen, Germany