Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation.

You, Suhang; Reyes, Mauricio (2022). Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation. Frontiers in neuroimaging, 1(1012639), p. 1012639. Frontiers 10.3389/fnimg.2022.1012639

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Contrast and texture modifications applied during training or test-time have recently shown promising results to enhance the generalization performance of deep learning segmentation methods in medical image analysis. However, a deeper understanding of this phenomenon has not been investigated. In this study, we investigated this phenomenon using a controlled experimental setting, using datasets from the Human Connectome Project and a large set of simulated MR protocols, in order to mitigate data confounders and investigate possible explanations as to why model performance changes when applying different levels of contrast and texture-based modifications. Our experiments confirm previous findings regarding the improved performance of models subjected to contrast and texture modifications employed during training and/or testing time, but further show the interplay when these operations are combined, as well as the regimes of model improvement/worsening across scanning parameters. Furthermore, our findings demonstrate a spatial attention shift phenomenon of trained models, occurring for different levels of model performance, and varying in relation to the type of applied image modification.

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