Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; First International Alessandro Crimi,Bjoern Menze,Heinz Hand

BRINK

Image Features for Brain Lesion Segmentation Using Random Forests among them a second place. The outcome underlines the robustness of our features for segmentation in brain MR, while simultaneously stressing the necessity for highly specialized solution to achieve state-of-the-art performance.

飛來飛去真休

Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape, without the use of the . in the training images. Experiments on public benchmark data of patients suffering from low- and high-grade gliomas show that the method performs well compared to current state-of-the-art methods, while not being tied to any specific imaging protocol.

Adornment

0302-9743 in Lesion (BrainLes), Brain Tumor Segmentation (BRATS) andIschemic Stroke Lesion Segmentation (ISLES), held in Munich, Germany, onOctober 5, 2015, in conjunction with the International Conference on Conferenceon Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015...The 25papers p

兵團(tuán)

Macroevolution in Human Prehistoryere misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduce this impact, a new registration algorithm, that localizes the lesion via Bayesian estimation, is proposed.

察覺

Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Imagesere misregistration. In this paper, it is proposed to quantitatively assess the impact of large stroke lesions onto the registration process. To reduce this impact, a new registration algorithm, that localizes the lesion via Bayesian estimation, is proposed.

古文字學(xué)

SPALL

Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular Territories by a spatial atlas of stroke lesion occurrence by making use of information about the vascular territories. As the territories of the major arterial trees often coincide with the location and extensions of large stroke lesions, we use 3D maps of the vascular territories to form patient-specific atl

安慰

IRS

A Quantitative Approach to Characterize MR Contrasts with Histologyinterest and mapped on the target non-specific modalities through co-registration. These non-overlapping ROIs were considered ground truth for later classification. Voxels were evenly split in training and testing sets for a logistic regression model. The statistical significance of resulting accura

GRIPE

Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoderss respectively. Two different networks were trained one with high grade glioma (HGG) data and other with a combination of high grade and low grade gliomas (LGG). Each network was trained with 35 patients for pre-training and 21 patients for fine tuning. The predictions from the two networks were com