| Brain Lesions, Introduction |
|
1 | (8) |
|
|
|
Brain Lesion Image Analysis |
|
|
|
Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models |
|
|
9 | (12) |
|
|
|
|
|
Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular Territories |
|
|
21 | (12) |
|
|
|
|
|
|
|
|
|
|
|
|
|
Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithms |
|
|
33 | (12) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Combining Unsupervised and Supervised Methods for Lesion Segmentation |
|
|
45 | (12) |
|
|
|
|
|
|
|
|
|
|
|
Assessment of Tissue Injury in Severe Brain Trauma |
|
|
57 | (12) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR Sequences |
|
|
69 | (11) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
A Semi-automatic Method for Segmentation of Multiple Sclerosis Lesions on Dual-Echo Magnetic Resonance Images |
|
|
80 | (11) |
|
|
|
|
|
|
|
|
|
|
|
Bayesian Stroke Lesion Estimation for Automatic Registration of DTI Images |
|
|
91 | (13) |
|
|
|
|
|
|
|
A Quantitative Approach to Characterize MR Contrasts with Histology |
|
|
104 | (15) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Brain Tumor Image Segmentation |
|
|
|
Image Features for Brain Lesion Segmentation Using Random Forests |
|
|
119 | (12) |
|
|
|
|
|
|
|
Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRI |
|
|
131 | (13) |
|
|
|
|
|
|
|
|
|
GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation |
|
|
144 | (12) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors |
|
|
156 | (12) |
|
|
|
|
|
|
|
|
|
|
|
Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape |
|
|
168 | (13) |
|
|
|
|
|
|
|
|
|
|
|
Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders |
|
|
181 | (14) |
|
|
|
Subramaniam Thirunavukkarasu |
|
|
|
|
|
|
A Convolutional Neural Network Approach to Brain Tumor Segmentation |
|
|
195 | (16) |
|
|
|
|
|
|
|
|
|
|
|
Ischemic Stroke Lesion Image Segmentation |
|
|
|
|
|
|
ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual Clustering |
|
|
211 | (11) |
|
|
|
|
|
|
|
Stroke Lesion Segmentation of 3D Brain MRI Using Multiple Random Forests and 3D Registration |
|
|
222 | (11) |
|
|
|
|
|
Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level Set |
|
|
233 | (13) |
|
|
|
|
|
|
|
ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images |
|
|
246 | (8) |
|
|
|
|
|
|
|
A Voxel-Wise, Cascaded Classification Approach to Ischemic Stroke Lesion Segmentation |
|
|
254 | (12) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Automatic Ischemic Stroke Lesion Segmentation in Multi-spectral MRI Images Using Random Forests Classifier |
|
|
266 | (9) |
|
|
|
|
|
Segmenting the Ischemic Penumbra: A Decision Forest Approach with Automatic Threshold Finding |
|
|
275 | (9) |
|
|
|
|
|
|
|
|
|
Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion Segmentation |
|
|
284 | (13) |
|
|
|
|
|
|
|
|
| Author Index |
|
297 | |
Biežāk uzdotie jautājumi par e-grāmatām