Abstract is missing.
- Brain Lesions, IntroductionAlessandro Crimi. 1-5 [doi]
- Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic ModelsOula Puonti, Koen Van Leemput. 9-20 [doi]
- Stroke Lesion Segmentation Using a Probabilistic Atlas of Cerebral Vascular TerritoriesAlexandra Derntl, Claudia Plant, Philipp Gruber, Susanne Wegener, Jan S. Bauer, Bjoern H. Menze. 21-32 [doi]
- Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography AlgorithmsEmily L. Dennis, Gautam Prasad, Madelaine Daianu, Liang Zhan, Talin Babikian, Claudia Kernan, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C. Giza, Robert F. Asarnow, Paul M. Thompson. 33-44 [doi]
- Combining Unsupervised and Supervised Methods for Lesion SegmentationTim Jerman, Alfiia Galimzianova, Franjo Pernus, Bostjan Likar, Ziga Spiclin. 45-56 [doi]
- Assessment of Tissue Injury in Severe Brain TraumaChristophe Maggia, Senan Doyle, Florence Forbes, Olivier Heck, Irène Troprès, Corentin Berthet, Yann Teyssier, Lionel Velly, Jean-François Payen, Michel Dojat. 57-68 [doi]
- A Nonparametric Growth Model for Brain Tumor Segmentation in Longitudinal MR SequencesEsther Alberts, Guillaume Charpiat, Yuliya Tarabalka, Thomas Huber, Marc-André Weber, Jan Bauer, Claus Zimmer, Bjoern H. Menze. 69-79 [doi]
- A Semi-automatic Method for Segmentation of Multiple Sclerosis Lesions on Dual-Echo Magnetic Resonance ImagesLoredana Storelli, Elisabetta Pagani, Maria Assunta Rocca, Mark A. Horsfield, Massimo Filippi. 80-90 [doi]
- Bayesian Stroke Lesion Estimation for Automatic Registration of DTI ImagesFélix Renard, Matthieu Urvoy, Assia Jaillard. 91-103 [doi]
- A Quantitative Approach to Characterize MR Contrasts with HistologyYaël Balbastre, Michel E. Vandenberghe, Anne-Sophie Hérard, Pauline Gipchtein, Caroline Jan, Anselme L. Perrier, Philippe Hantraye, Romina Aron-Badin, Jean-François Mangin, Thierry Delzescaux. 104-115 [doi]
- Image Features for Brain Lesion Segmentation Using Random ForestsOskar Maier, Matthias Wilms, Heinz Handels. 119-130 [doi]
- Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRISérgio Pereira, Adriano Pinto, Victor Alves, Carlos A. Silva. 131-143 [doi]
- GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma SegmentationSpyridon Bakas, Ke Zeng, Aristeidis Sotiras, Saima Rathore, Hamed Akbari, Bilwaj Gaonkar, Martin Rozycki, Sarthak Pati, Christos Davatzikos. 144-155 [doi]
- Parameter Learning for CRF-Based Tissue Segmentation of Brain TumorsRaphael Meier, Venetia Karamitsou, Simon Habegger, Roland Wiest, Mauricio Reyes. 156-167 [doi]
- Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor ShapeMikael Agn, Oula Puonti, Per Munck af Rosenschöld, Ian Law, Koen Van Leemput. 168-180 [doi]
- Multi-modal Brain Tumor Segmentation Using Stacked Denoising AutoencodersKiran Vaidhya, Subramaniam Thirunavukkarasu, Alex Varghese, Ganapathy Krishnamurthi. 181-194 [doi]
- A Convolutional Neural Network Approach to Brain Tumor SegmentationMohammad Havaei, Francis Dutil, Chris Pal, Hugo Larochelle, Pierre-Marc Jodoin. 195-208 [doi]
- ISLES (SISS) Challenge 2015: Segmentation of Stroke Lesions Using Spatial Normalization, Random Forest Classification and Contextual ClusteringHanna-Leena Halme, Antti Korvenoja, Eero Salli. 211-221 [doi]
- Stroke Lesion Segmentation of 3D Brain MRI Using Multiple Random Forests and 3D RegistrationChing-Wei Wang, Jia-Hong Lee. 222-232 [doi]
- Segmentation of Ischemic Stroke Lesions in Multi-spectral MR Images Using Weighting Suppressed FCM and Three Phase Level SetChaolu Feng, Dazhe Zhao, Min Huang. 233-245 [doi]
- Automatic Ischemic Stroke Lesion Segmentation in Multi-spectral MRI Images Using Random Forests ClassifierQaiser Mahmood, Abdul Basit. 266-274 [doi]
- Segmenting the Ischemic Penumbra: A Decision Forest Approach with Automatic Threshold FindingRichard McKinley, Levin Häni, Roland Wiest, Mauricio Reyes. 275-283 [doi]
- Input Data Adaptive Learning (IDAL) for Sub-acute Ischemic Stroke Lesion SegmentationMichael Götz, Christian Weber, Christoph Kolb, Klaus H. Maier-Hein. 284-295 [doi]