Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; Third International Alessandro Crimi,Spyridon Bakas,Mauricio

CAB

Leticia Reyes,Bryce Wolfe,Thaddeus Golosg rank?#1 in the ISBI 2015 longitudinal multiple sclerosis lesion segmentation challenge, we show that a setup which combines these techniques can outperform the state of the art in automated lesion segmentation.

d-limonene

Leticia Reyes,Bryce Wolfe,Thaddeus Golosodalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by . (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.

Mettle

Polarizing Macrophages In Vitro,epresentation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.

不可比擬

使成波狀

Xia Zhang,Justin P. Edwards,David M. Mossernt Post-Concussive Symptoms in a small dataset but achieve only a .74 AUC in identifying mTBI subjects with milder symptoms. Finally, we perform subject-specific simulations which characterize which injuries are detected and which are missed.

Synchronism

Mario R. Escobar,Herman FriedmanFlair sequences are used to populate our model, and a variational approach is implemented to find a solution. The performance of our model is demonstrated on two datasets, and compared to manual delineations by expert raters.

conjunctivitis

泰然自若

Automated Segmentation of Multiple Sclerosis Lesions Using Multi-dimensional Gated Recurrent Unitsg rank?#1 in the ISBI 2015 longitudinal multiple sclerosis lesion segmentation challenge, we show that a setup which combines these techniques can outperform the state of the art in automated lesion segmentation.

仇恨

Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentationodalities. The addition of JIF synthesized images improved the Dice-Sorensen coefficient (relative to manually drawn gold standards) of lesion segmentations over the standard model segmentations by . (mean ± standard deviation) at optimal threshold over all subjects and 10 separate training/testing folds.

水汽

Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Frameworkepresentation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.