  The segmentation of the breast from the chest wall is an important first step
in the analysis of breast magnetic resonance images. 3D U-nets have been shown
to obtain high segmentation accuracy and appear to generalize well when trained
on one scanner type and tested on another scanner, provided that a very similar
T1-weighted MR protocol is used. There has, however, been little work
addressing the problem of domain adaptation when image intensities or patient
orientation differ markedly between the training set and an unseen test set. To
overcome the domain shift we propose to apply extensive intensity augmentation
in addition to geometric augmentation during training. We explored both style
transfer and a novel intensity remapping approach as intensity augmentation
strategies. For our experiments, we trained a 3D U-net on T1-weighted scans and
tested on T2-weighted scans. By applying intensity augmentation we increased
segmentation performance from a DSC of 0.71 to 0.90. This performance is very
close to the baseline performance of training and testing on T2-weighted scans
(0.92). Furthermore, we applied our network to an independent test set made up
of publicly available scans acquired using a T1-weighted TWIST sequence and a
different coil configuration. On this dataset we obtained a performance of
0.89, close to the inter-observer variability of the ground truth segmentations
(0.92). Our results show that using intensity augmentation in addition to
geometric augmentation is a suitable method to overcome the intensity domain
shift and we expect it to be useful for a wide range of segmentation tasks.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning;Machine Learning