Improving Generalizability in X-Ray Segmentation of the femur
Evaluating the Impact of Traditional Data Augmentation Techniques on the generalizability across Datasets
R. Bockholt (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.A. van den Berg – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
G. van Tulder – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.H. Krijthe – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
Xucong Zhang – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
An accurate segmentation model for hip compo- nents could improve the diagnosis of Osteoarthritis, a prevalent age-related condition affecting joints. A significant challenge in developing effective and robust segmentation models are the domain differ- ences across various datasets. In this study, we in- vestigate the impact of different data augmentation and preprocessing techniques on the generalizabil- ity of femur segmentation models across datasets. Using two labeled datasets, we evaluate the perfor- mance of a U-Net segmentation model, focusing on the effectiveness of augmentations like image flip- ping, random rotations, blur, contrast, and bright- ness adjustments. Our findings reveal that certain augmentations, particularly random rotations of up to 15 degrees, vertical image flipping and light blurring, significantly improve the model’s gener- alization to another data set, reducing boundary er- rors and enhancing segmentation accuracy. These results underscore the potential of targeted data augmentations in developing robust, generalizable models for hip joint component segmentation.