Abstract is missing.
- Fully Automated Patch-Based Image Restoration: Application to Pathology InpaintingFerran Prados, M. Jorge Cardoso, Niamh Cawley, Baris Kanber, Olga Ciccarelli, Claudia A. M. Wheeler-Kingshott, Sébastien Ourselin. 3-15 [doi]
- Towards a Second Brain Images of Tumours for Evaluation (BITE2) DatabaseIan J. Gerard, C. Couturier, Marta Kersten-Oertel, Simon Drouin, Dante De Nigris, Jeffery A. Hall, Kelvin Mok, Kevin Petrecca, Tal Arbel, D. Louis Collins. 16-22 [doi]
- Topological Measures of Connectomics for Low Grades GliomaBenjamin Amoah, Alessandro Crimi. 23-31 [doi]
- Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain InjuryEmily L. Dennis, Faisal Rashid, Julio Villalon-Reina, Gautam Prasad, Joshua Faskowitz, Talin Babikian, Richard Mink, Christopher Babbitt, Jeffrey Johnson, Christopher C. Giza, Robert F. Asarnow, Paul M. Thompson. 32-42 [doi]
- An Online Platform for the Automatic Reporting of Multi-parametric Tissue Signatures: A Case Study in GlioblastomaJavier Juan-Albarracín, Elies Fuster-García, Juan Miguel García-Gómez. 43-51 [doi]
- A Fast Approach to Automatic Detection of Brain LesionsSubhranil Koley, Chandan Chakraborty, Caterina Mainero, Bruce Fischl, Iman Aganj. 52-61 [doi]
- Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease ProgressionRamandeep S. Randhawa, Ankit Modi, Parag Jain, Prashant Warier. 65-74 [doi]
- Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random FieldsXiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yong Fan, Yazhuo Zhang. 75-87 [doi]
- Brain Tumor Segmentation with Optimized Random ForestLászló Lefkovits, Szidónia Lefkovits, László Szilágyi. 88-99 [doi]
- CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking BiasRaphael Meier, Urspeter Knecht, Roland Wiest, Mauricio Reyes. 100-107 [doi]
- Fully Convolutional Deep Residual Neural Networks for Brain Tumor SegmentationPeter D. Chang. 108-118 [doi]
- Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image SegmentationRichard McKinley, Rik Wepfer, Tom Gundersen, Franca Wagner, Andrew Chan, Roland Wiest, Mauricio Reyes. 119-128 [doi]
- Brain Tumor Segmantation Using Random Forest Trained on Iteratively Selected PatientsAbdelrahman Ellwaa, Ahmed Hussein, Essam AlNaggar, Mahmoud Zidan, Michael Zaki, Mohamed A. Ismail, Nagia M. Ghanem. 129-137 [doi]
- DeepMedic for Brain Tumor SegmentationKonstantinos Kamnitsas, Enzo Ferrante, Sarah Parisot, Christian Ledig, Aditya V. Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker. 138-149 [doi]
- 3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution ArchitecturesAdria Casamitjana, Santi Puch, Asier Aduriz, Verónica Vilaplana. 150-161 [doi]
- Anatomy-Guided Brain Tumor Segmentation and ClassificationBi Song, Chen-Rui Chou, Xiaojing Chen, Albert Huang, Ming-Chang Liu. 162-170 [doi]
- Lifted Auto-Context Forests for Brain Tumour SegmentationLoïc Le Folgoc, Aditya V. Nori, Siddharth Ancha, Antonio Criminisi. 171-183 [doi]
- Segmentation of Gliomas in Pre-operative and Post-operative Multimodal Magnetic Resonance Imaging Volumes Based on a Hybrid Generative-Discriminative FrameworkKe Zeng, Spyridon Bakas, Aristeidis Sotiras, Hamed Akbari, Martin Rozycki, Saima Rathore, Sarthak Pati, Christos Davatzikos. 184-194 [doi]
- Interactive Semi-automated Method Using Non-negative Matrix Factorization and Level Set Segmentation for the BRATS ChallengeDimah Dera, Fabio Raman, Nidhal Bouaynaya, Hassan M. Fathallah-Shaykh. 195-205 [doi]
- Brain Tumor Segmentation by Variability Characterization of Tumor BoundariesEdgar A. Rios Piedra, Benjamin M. Ellingson, Ricky K. Taira, Suzie El-Saden, Alex A. T. Bui, William Hsu. 206-216 [doi]
- Predicting Stroke Lesion and Clinical Outcome with Random ForestsOskar Maier, Heinz Handels. 219-230 [doi]
- Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic StrokeYoungwon Choi, Yongchan Kwon, Han-Byul Lee, Beomjoon Kim, Myunghee Cho Paik, Joong-Ho Won. 231-243 [doi]
- Prediction of Ischemic Stroke Lesion and Clinical Outcome in Multi-modal MRI Images Using Random ForestsQaiser Mahmood, Abdul Basit. 244-255 [doi]
- Combining Deep Learning Networks with Permutation Tests to Predict Traumatic Brain Injury OutcomeYunliang Cai, Songbai Ji. 259-270 [doi]
- Mild Traumatic Brain Injury Outcome Prediction Based on Both Graph and K-nn MethodsRoberto Bellotti, Angela Lombardi, Cataldo Guaragnella, Nicola Amoroso, Andrea Tateo, Sabina Tangaro. 271-281 [doi]
- Unsupervised 3-D Feature Learning for Mild Traumatic Brain InjuryPo-Yu Kao, Eduardo Rojas, Jefferson W. Chen, Angela Zhang, B. S. Manjunath. 282-290 [doi]