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

Autor: Gustav Müller-Franzes, Fritz Müller-Franzes, Luisa Huck, Vanessa Raaff, Eva Kemmer, Firas Khader, Soroosh Tayebi Arasteh, Teresa Lemainque, Jakob Nikolas Kather, Sven Nebelung, Christiane Kuhl, Daniel Truhn
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-41331-x
Popis: 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
Databáze: Directory of Open Access Journals
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