Mixup and adversarial domain adaptation each improve the robustness of knee MRI cartilage segmentation on data from a different scanner, with mixup matching domain adaptation's gains at lower cost.
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks
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abstract
High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications. While manual segmentation is accepted as the gold-standard, recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for efficient automatic segmentation of soft tissues. However, due to the stochastic nature of deep learning and the multitude of hyperparameters in training networks, predicting network behavior is challenging. In this paper, we quantify the impact of three factors associated with CNN segmentation performance: network architecture, training loss functions, and training data characteristics. We evaluate the impact of these variations on the segmentation of femoral cartilage and propose potential modifications to CNN architectures and training protocols to train these models with confidence.
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Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
Mixup and adversarial domain adaptation each improve the robustness of knee MRI cartilage segmentation on data from a different scanner, with mixup matching domain adaptation's gains at lower cost.