Abstract is missing.
- Multimodal Patho-Connectomics of Brain InjuryRagini Verma, Yusuf Osmanlioglu, Abdol Aziz Ould Ismail. 3-14 [doi]
- CT Brain Perfusion: A Clinical PerspectiveArsany Hakim, Roland Wiest. 15-24 [doi]
- Adverse Effects of Image Tiling on Convolutional Neural NetworksG. Anthony Reina, Ravi Panchumarthy. 25-36 [doi]
- An Update on Machine Learning in Neuro-Oncology DiagnosticsThomas C. Booth. 37-44 [doi]
- MIMoSA: An Approach to Automatically Segment T2 Hyperintense and T1 Hypointense Lesions in Multiple SclerosisAlessandra M. Valcarcel, Kristin A. Linn, Fariha Khalid, Simon N. Vandekar, Shahamat Tauhid, Theodore D. Satterthwaite, John Muschelli, Rohit Bakshi, Russell T. Shinohara. 47-56 [doi]
- CNN Prediction of Future Disease Activity for Multiple Sclerosis Patients from Baseline MRI and Lesion LabelsNazanin Mohammadi Sepahvand, Tal Hassner, Douglas L. Arnold, Tal Arbel. 57-69 [doi]
- Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial NetworksTony C. W. Mok, Albert C. S. Chung. 70-80 [doi]
- Multipath Densely Connected Convolutional Neural Network for Brain Tumor SegmentationCong Liu 0009, Weixin Si, Yinling Qian, Xiangyun Liao, Qiong Wang 0001, Yong Guo, Pheng-Ann Heng. 81-91 [doi]
- Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor SegmentationMicah J. Sheller, G. Anthony Reina, Brandon Edwards, Jason Martin, Spyridon Bakas. 92-104 [doi]
- Patient-Specific Registration of Pre-operative and Post-recurrence Brain Tumor MRI ScansXu Han, Spyridon Bakas, Roland Kwitt, Stephen R. Aylward, Hamed Akbari, Michel Bilello, Christos Davatzikos, Marc Niethammer. 105-114 [doi]
- Segmentation of Post-operative Glioblastoma in MRI by U-Net with Patient-Specific Interactive RefinementAshis Kumar Dhara, Kalyan Ram Ayyalasomayajula, Erik Arvids, Markus Fahlström, Johan Wikström, Elna-Marie Larsson, Robin Strand. 115-122 [doi]
- Characterizing Peritumoral Tissue Using DTI-Based Free Water EliminationAbdol Aziz Ould Ismail, Drew Parker, Moisés Hernández-Fernández, Steven Brem, Simon Alexander, Ofer Pasternak, Emmanuel Caruyer, Ragini Verma. 123-131 [doi]
- Deep 2D Encoder-Decoder Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation in Brain MRIShahab Aslani, Michael Dayan, Vittorio Murino, Diego Sona. 132-141 [doi]
- Shallow vs Deep Learning Architectures for White Matter Lesion Segmentation in the Early Stages of Multiple SclerosisFrancesco La Rosa, Mário João Fartaria, Tobias Kober, Jonas Richiardi, Cristina Granziera, Jean-Philippe Thiran, Meritxell Bach Cuadra. 142-151 [doi]
- Detection of Midline Brain Abnormalities Using Convolutional Neural NetworksAleix Solanes, Joaquim Radua, Laura Igual. 152-160 [doi]
- Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR ImagesChristoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab. 161-169 [doi]
- Brain Tumor Detection and Classification from Multi-sequence MRI: Study Using ConvNetsSubhashis Banerjee, Sushmita Mitra, Francesco Masulli, Stefano Rovetta. 170-179 [doi]
- Voxel-Wise Comparison with a-contrario Analysis for Automated Segmentation of Multiple Sclerosis Lesions from Multimodal MRIFrancesca Galassi, Olivier Commowick, Emmanuel Vallee, Christian Barillot. 180-188 [doi]
- A Graph Based Similarity Measure for Assessing Altered Connectivity in Traumatic Brain InjuryYusuf Osmanlioglu, Jacob A. Alappatt, Drew Parker, Junghoon Kim, Ragini Verma. 189-198 [doi]
- Multi-scale Convolutional-Stack Aggregation for Robust White Matter Hyperintensities SegmentationHongwei Li, Jianguo Zhang, Mark Mühlau, Jan Kirschke, Bjoern H. Menze. 199-207 [doi]
- Holistic Brain Tumor Screening and Classification Based on DenseNet and Recurrent Neural NetworkYufan Zhou, Zheshuo Li, Hong Zhu, Changyou Chen, Mingchen Gao, Kai Xu, Jinhui Xu. 208-217 [doi]
- 3D Texture Feature Learning for Noninvasive Estimation of Gliomas Pathological SubtypeGuoqing Wu, Yuanyuan Wang, Jinhua Yu. 218-227 [doi]
- Pathology Segmentation Using Distributional Differences to Images of Healthy OriginSimon Andermatt, Antal Horváth, Simon Pezold, Philippe C. Cattin. 228-238 [doi]
- Multi-stage Association Analysis of Glioblastoma Gene Expressions with Texture and Spatial PatternsSamar S. M. Elsheikh, Spyridon Bakas, Nicola J. Mulder, Emile R. Chimusa, Christos Davatzikos, Alessandro Crimi. 239-250 [doi]
- Stroke Lesion Segmentation with 2D Novel CNN Pipeline and Novel Loss FunctionPengbo Liu. 253-262 [doi]
- Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image ModalitiesJose Dolz, Ismail Ben Ayed, Christian Desrosiers. 271-282 [doi]
- Multi-scale Deep Convolutional Neural Network for Stroke Lesions Segmentation on CT ImagesLiangliang Liu, Shuai Yang, Li Meng, Min Li 0007, Jianxin Wang. 283-291 [doi]
- Ischemic Stroke Lesion Segmentation Using Adversarial LearningMobarakol Islam, N. Rajiv Vaidyanathan, V. Jeya Maria Jose, Hongliang Ren 0001. 292-300 [doi]
- V-Net and U-Net for Ischemic Stroke Lesion Segmentation in a Small Dataset of Perfusion DataGustavo Retuci Pinheiro, Raphael Voltoline, Mariana P. Bento, Letícia Rittner. 301-309 [doi]
- Integrated Extractor, Generator and Segmentor for Ischemic Stroke Lesion SegmentationTao Song, Ning Huang. 310-318 [doi]
- ISLES Challenge: U-Shaped Convolution Neural Network with Dilated Convolution for 3D Stroke Lesion SegmentationAlzbeta Tureckova, Antonio J. Rodríguez-Sánchez. 319-327 [doi]
- Fully Automatic Segmentation for Ischemic Stroke Using CT Perfusion MapsVikas Kumar Anand, Mahendra Khened, Alex Varghese, Ganapathy Krishnamurthi. 328-334 [doi]
- Combining Good Old Random Forest and DeepLabv3+ for ISLES 2018 CT-Based Stroke SegmentationLasse Böhme, Frederic Madesta, Thilo Sentker, René Werner. 335-342 [doi]
- Volumetric Adversarial Training for Ischemic Stroke Lesion SegmentationHao-Yu Yang. 343-351 [doi]
- Ischemic Stroke Lesion Segmentation in CT Perfusion Scans Using Pyramid Pooling and Focal LossSayed Mazdak Abulnaga, Jonathan Rubin. 352-363 [doi]
- MixNet: Multi-modality Mix Network for Brain SegmentationLong Chen, Dorit Merhof. 367-377 [doi]
- A Skip-Connected 3D DenseNet Networks with Adversarial Training for Volumetric SegmentationToan Duc Bui, Sang-il Ahn, Yong Woo Lee, Jitae Shin. 378-384 [doi]
- Automatic Brain Structures Segmentation Using Deep Residual Dilated U-NetHongwei Li, Andrii Zhygallo, Bjoern H. Menze. 385-393 [doi]
- 3D Patchwise U-Net with Transition Layers for MR Brain SegmentationMiguel Luna, Sang-Hyun Park. 394-403 [doi]
- Dropout-Enabled Ensemble Learning for Multi-scale Biomedical DataAlexandre Momeni, Marc Thibault, Olivier Gevaert. 407-415 [doi]
- A Combined Radio-Histological Approach for Classification of Low Grade GliomasAditya Bagari, Ashish Kumar, Avinash Kori, Mahendra Khened, Ganapathy Krishnamurthi. 416-427 [doi]
- Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNNXinpeng Xie, Yuexiang Li, Menglu Zhang, LinLin Shen. 428-436 [doi]
- Synthetic Perfusion Maps: Imaging Perfusion Deficits in DSC-MRI with Deep LearningAndreas Hess, Raphael Meier, Johannes Kaesmacher, Simon Jung, Fabien Scalzo, David S. Liebeskind, Roland Wiest, Richard McKinley. 447-455 [doi]
- ICHNet: Intracerebral Hemorrhage (ICH) Segmentation Using Deep LearningMobarakol Islam, Parita Sanghani, Angela An Qi See, Michael Lucas James, Nicolas Kon Kam King, Hongliang Ren 0001. 456-463 [doi]
- Can Diffusion MRI Reveal Stroke-Induced Microstructural Changes in GM?Lorenza Brusini, Ilaria Boscolo Galazzo, Mauro Zucchelli, Cristina Granziera, Gloria Menegaz. 464-471 [doi]