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Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients.

, , , , , , , , and . Patch-MI@MICCAI, volume 10530 of Lecture Notes in Computer Science, page 20-28. Springer, (2017)

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Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging., , , , , , , and . NeuroImage, (2016)Whole Brain Parcellation with Pathology: Validation on Ventriculomegaly Patients., , , , , , , , and . Patch-MI@MICCAI, volume 10530 of Lecture Notes in Computer Science, page 20-28. Springer, (2017)Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation., , , , , , and . SASHIMI@MICCAI, volume 11037 of Lecture Notes in Computer Science, page 20-30. Springer, (2018)Efficient multi-atlas registration using an intermediate template image., , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 10137 of SPIE Proceedings, page 101371F. SPIE, (2017)Self Super-Resolution for Magnetic Resonance Images using Deep Networks., , , and . CoRR, (2018)Evaluating the impact of intensity normalization on MR image synthesis., , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 109493H. SPIE, (2019)Multi-atlas segmentation of the hydrocephalus brain using an adaptive ventricle atlas., , , , , , and . Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 10578 of SPIE Proceedings, page 105780F. SPIE, (2018)Evaluating the Impact of Intensity Normalization on MR Image Synthesis., , , and . CoRR, (2018)A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRI., , , , , , , , , and . MICCAI (1), volume 11070 of Lecture Notes in Computer Science, page 100-108. Springer, (2018)