Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

Deep learning (DL)-based models have demonstrated good performance in medical image segmentation . However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations . In this work, we present a random style transfer network to tackle the domain generalization problem . Style transfer is used to generate training data with a wider distribution/ heterogeneity, namely domain augmentation . The model can be trained in a semi-supervised manner by simultaneously optimizing a supervised segmentation and an unsupervised style translation objective . We evaluated the proposed framework on 40 subjects from the M\&Ms challenge2020, and obtained promising performance in the segmentation for data from unknown vendors and centers . The framework incorporates the spatial information and shape prior of the target by introducing two regularization terms. Besides, the framework incorporates

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Keywords : style - domain - transfer - framework - segmentation -

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