  Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosis
of pelvic bone diseases and for planning patient-specific hip surgeries. With
the emergence and advancements of deep learning for digital healthcare, several
methodologies have been proposed for such segmentation tasks. But in a low data
scenario, the lack of abundant data needed to train a Deep Neural Network is a
significant bottle-neck. In this work, we propose a methodology based on
modulation of image tonal distributions and deep learning to boost the
performance of networks trained on limited data. The strategy involves
pre-processing of test data through histogram specification. This simple yet
effective approach can be viewed as a style transfer methodology. The
segmentation task uses a U-Net configuration with an EfficientNet-B0 backbone,
optimized using an augmented BCE-IoU loss function. This configuration is
validated on a total of 284 images taken from two publicly available CT
datasets, TCIA (a cancer imaging archive) and the Visible Human Project. The
average performance measures for the Dice coefficient and Intersection over
Union are 95.7% and 91.9%, respectively, give strong evidence for the
effectiveness of the approach, which is highly competitive with
state-of-the-art methodologies.
