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Accurate segmentation of the pelvic CTs is crucial for the clinical diagnosisof pelvic bone diseases and for planning patient-specific hip surgeries. Withthe emergence and advancements of deep learning for digital healthcare, severalmethodologies have been proposed for such segmentation tasks. But in a low datascenario, the lack of abundant data needed to train a Deep Neural Network is asignificant bottle-neck. In this work, we propose a methodology based onmodulation of image tonal distributions and deep learning to boost theperformance of networks trained on limited data. The strategy involvespre-processing of test data through histogram specification. This simple yeteffective approach can be viewed as a style transfer methodology. Thesegmentation task uses a U-Net configuration with an EfficientNet-B0 backbone,optimized using an augmented BCE-IoU loss function. This configuration isvalidated on a total of 284 images taken from two publicly available CTdatasets, TCIA (a cancer imaging archive) and the Visible Human Project. Theaverage performance measures for the Dice coefficient and Intersection overUnion are 95.7% and 91.9%, respectively, give strong evidence for theeffectiveness of the approach, which is highly competitive withstate-of-the-art methodologies.

Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation