Research data associated with paper: A Semantic Segmentation Model for Lumbar MRI Images using Divergence Loss, comprising the python code, a trained model and empirical results. 

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<address>Designed Shim Coil Layouts</address><address>Use Visualize_Coils.m with Matlab 2018a can visualize SH coils.</address>

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Example axial and coronal phase maps and post-treatment MRI from 68 thalamotomies in essential tremor patients and four pallidotomies in Parkinson's disease patients. From the manuscript "Using phase data from MR temperature imaging to visualize anatomy during MRI-guided focused ultrasound neurosurgery" published in 2020 in IEEE Trans. Med. Imaging.

 

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The following pages show axial T2-weighted MRI obtained at 24 hours and at 3-15 months after MRgFUS. The images shown here were registered to the same reference frame that was used in the thermal simulations; every third image is shown. To segment the bone marrow lesions, the registered images were toggled back and forth between the two time points to detect obvious changes. The lesion segmentations were completed before the acoustic and thermal simulations were performed. They were originally done on the native T2-weighted images acquired at 3-15 months after FUS.

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BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’19 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms.

Last Updated On: 
Fri, 02/28/2020 - 06:31