  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$\pm$0.069 versus 0.916$\pm$0.067, P<0.001) and on the external
testset (0.824$\pm$0.144 versus 0.864$\pm$0.081, P=0.004). Moreover, the
average symmetric surface distance was higher (=worse) for nnUNet than for
TraBS on the internal (0.657$\pm$2.856 versus 0.548$\pm$2.195, P=0.001) and on
the external testset (0.727$\pm$0.620 versus 0.584$\pm$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.
