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Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
The findings of splenomegaly, abnormal enlargement of the spleen, is a n...
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Inter-Scanner Harmonization of High Angular Resolution DW-MRI using Null Space Deep Learning
Diffusion-weighted magnetic resonance imaging (DW-MRI) allows for non-in...
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Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-M...
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Improved Stability of Whole Brain Surface Parcellation with Multi-Atlas Segmentation
Whole brain segmentation and cortical surface parcellation are essential...
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Generalizing Deep Whole Brain Segmentation for Pediatric and Post-Contrast MRI with Augmented Transfer Learning
Generalizability is an important problem in deep neural networks, especi...
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Brain Modelling as a Service: The Virtual Brain on EBRAINS
The Virtual Brain (TVB) is now available as open-source cloud ecosystem ...
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Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI...
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Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Whole brain segmentation on structural magnetic resonance imaging (MRI) is essential for understanding neuroanatomical-functional relationships. Traditionally, multi-atlas segmentation has been regarded as the standard method for whole brain segmentation. In past few years, deep convolutional neural network (DCNN) segmentation methods have demonstrated their advantages in both accuracy and computational efficiency. Recently, we proposed the spatially localized atlas network tiles (SLANT) method, which is able to segment a 3D MRI brain scan into 132 anatomical regions. Commonly, DCNN segmentation methods yield inferior performance under external validations, especially when the testing patterns were not presented in the training cohorts. Recently, we obtained a clinically acquired, multi-sequence MRI brain cohort with 1480 clinically acquired, de-identified brain MRI scans on 395 patients using seven different MRI protocols. Moreover, each subject has at least two scans from different MRI protocols. Herein, we assess the SLANT method's intra- and inter-protocol reproducibility. SLANT achieved less than 0.05 coefficient of variation (CV) for intra-protocol experiments and less than 0.15 CV for inter-protocol experiments. The results show that the SLANT method achieved high intra- and inter- protocol reproducibility.
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