Fig. 1: Developing and testing the MindGlide model. | Nature Communications

Fig. 1: Developing and testing the MindGlide model.

From: Enabling new insights from old scans by repurposing clinical MRI archives for multiple sclerosis research

Fig. 1: Developing and testing the MindGlide model.

MindGlide model enables highly efficient and robust MRI segmentation. Segmenting and quantifying lesions on heterogeneous contrasts with minimal pre-processing (and no pre-processing required by the user). MindGlide model generalizes to tasks not used to train the model, such as segmenting T2-weighted and positron density MRI scans in unseen data sets. a Provides an overview of real (top) and augmented (bottom) training data. b, c Illustrate all parts of our training and fine-tuning pipeline. d Shows images of heterogenous contrasts used for testing MindGlide. FLAIR Fluid Attenuated Inversion Recovery, MRI magnetic resonance imaging.

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