Improved X-Ray Bone Segmentation by Normalization and Augmentation Strategies
X-ray images can show great variation in contrast and noise levels. In addition, important subject structures might be superimposed with surgical tools and implants. As medical image datasets tend to be of small size, these image characteristics are often under-represented. For the task of automated, learning-based segmentation of bone structures, this may lead to poor generalization towards unseen images and consequently limits practical application. In this work, we employ various data augmentation techniques that address X-ray-specific image characteristics and evaluate them on lateral projections of the femur bone. We combine those with data and feature normalization strategies that could prove beneficial to this domain. We show that instance normalization is a viable alternative to batch normalization and demonstrate that contrast scaling and the overlay of surgical tools and implants in the image domain can boost the representational capacity of available image data. By employing our best strategy, we can improve the average symmetric surface distance measure by 36:22 %.
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