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

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...

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Bibliographic Details
Published in:Scientific reports 2023-08, Vol.13 (1), p.14207-14207, Article 14207
Main Authors: Müller-Franzes, Gustav, Müller-Franzes, Fritz, Huck, Luisa, Raaff, Vanessa, Kemmer, Eva, Khader, Firas, Arasteh, Soroosh Tayebi, Lemainque, Teresa, Kather, Jakob Nikolas, Nebelung, Sven, Kuhl, Christiane, Truhn, Daniel
Format: Article
Language:English
Subjects:
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Breast
Humanities and Social Sciences
Image processing
Magnetic resonance imaging
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Segmentation
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Summary: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 
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-41331-x