  Yes, it can. Data augmentation is perhaps the oldest preprocessing step in
computer vision literature. Almost every computer vision model trained on
imaging data uses some form of augmentation. In this paper, we use the
inter-vertebral disk segmentation task alongside a deep residual U-Net as the
learning model, to explore the effectiveness of augmentation. In the extreme,
we observed that a model trained on patches extracted from just one scan, with
each patch augmented 50 times; achieved a Dice score of 0.73 in a validation
set of 40 cases. Qualitative evaluation indicated a clinically usable
segmentation algorithm, which appropriately segments regions of interest,
alongside limited false positive specks. When the initial patches are extracted
from nine scans the average Dice coefficient jumps to 0.86 and most of the
false positives disappear. While this still falls short of state-of-the-art
deep learning based segmentation of discs reported in literature, qualitative
examination reveals that it does yield segmentation, which can be amended by
expert clinicians with minimal effort to generate additional data for training
improved deep models. Extreme augmentation of training data, should thus be
construed as a strategy for training deep learning based algorithms, when very
little manually annotated data is available to work with. Models trained with
extreme augmentation can then be used to accelerate the generation of manually
labelled data. Hence, we show that extreme augmentation can be a valuable tool
in addressing scaling up small imaging data sets to address medical image
segmentation tasks.

Computer Vision and Pattern Recognition