Scanner Agnostic Ultrasound Image Interpretation.

Ultrasound scanners image the anatomy modulated by their characteristic texture. For certain anatomical regions such as the liver, the characteristic texture of the scanner itself becomes the anatomical marker. Deep Learning (DL) models trained on a scanner-type not only model the anatomical content, they also learn the scanner's characteristic texture. Portability of such models across scanner-types is affected by the learnt styles and results in suboptimal outcome (e.g., for segmentation models, lower Dice values when inferred on images procured from different scanner-type). Instead of retraining the DL model to accommodate this diversity, we transform the texture of the previously unseen data to match the training distribution. Neural style transfer in prior art has used features from the popular VGG network to accomplish this. We not only use a previously trained DL model for the image interpretation task e.g. segmentation, we also utilize its feature maps to accomplish style transfer as well, reducing the complexity of the algorithm pipeline. We demonstrate the improvement in segmentation outcome after such a such style transfer without retraining an existing model.