Abstract is missing.
- Glioma Diagnosis and Classification: Illuminating the Gold StandardMacLean P. Nasrallah. 3-10 [doi]
- Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based MethodsHuahong Zhang, Ipek Oguz. 11-29 [doi]
- Computational Diagnostics of GBM Tumors in the Era of Radiomics and RadiogenomicsAnahita Fathi Kazerooni, Christos Davatzikos. 30-38 [doi]
- Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional NetworksZhongqiang Liu, Dongdong Gu, Yu Zhang, Xiaohuan Cao, Zhong Xue. 41-50 [doi]
- Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmentation on Computed Tomographic AngiographyGuoqing Wu, Li Zhang 0040, Xi Chen, Jixian Lin, Yuanyuan Wang, Jinhua Yu. 51-59 [doi]
- Volume Preserving Brain Lesion SegmentationYanlin Liu, Xiangzhu Zeng, Chuyang Ye. 60-69 [doi]
- Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Primary Progressive Multiple SclerosisLorenza Brusini, Ilaria Boscolo Galazzo, Muge Akinci, Federica Cruciani, Marco Pitteri, Stefano Ziccardi, Albulena Bajrami, Marco Castellaro, Ahmed M. A. Salih, Francesca B. Pizzini, Jorge Jovicich, Massimiliano Calabrese, Gloria Menegaz. 70-79 [doi]
- Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor PathologyXiaofeng Liu, Fangxu Xing, Chao Yang 0011, C. C. Jay Kuo, Georges El Fakhri, Jonghye Woo. 80-91 [doi]
- Multivariate Analysis is Sufficient for Lesion-Behaviour MappingLucas Martin, Julie Josse, Bertrand Thirion. 92-100 [doi]
- Label-Efficient Multi-task Segmentation Using Contrastive LearningJunichiro Iwasawa, Yuichiro Hirano, Yohei Sugawara. 101-110 [doi]
- Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion SegmentationStefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim 0001, Nassir Navab. 111-121 [doi]
- MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases DetectionHui Yu, Wenjun Xia, Yan Liu, Xuejun Gu, Jiliu Zhou, Yi Zhang 0018. 122-132 [doi]
- Unsupervised 3D Brain Anomaly DetectionJaime Simarro Viana, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben, Diana Maria Sima. 133-142 [doi]
- Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRITejas Sudharshan Mathai, Yi Wang, Nathan M. Cross. 143-156 [doi]
- Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning RegressionSarthak Pati, Vaibhav Sharma, Heena Aslam, Siddhesh P. Thakur, Hamed Akbari, Andreas Mang, Shashank Subramanian, George Biros, Christos Davatzikos, Spyridon Bakas. 157-167 [doi]
- Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke LesionsJulian Klug, Guillaume Leclerc, Elisabeth Dirren, Maria Giulia Preti, Dimitri Van De Ville, Emmanuel Carrera. 168-180 [doi]
- Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI ImagesWen Jun, Haoxiang Xu, Zhang Wang. 183-193 [doi]
- Multimodal Brain Image Analysis and Survival Prediction Using Neuromorphic Attention-Based Neural NetworksIl Song Han. 194-206 [doi]
- Context Aware 3D UNet for Brain Tumor SegmentationParvez Ahmad, Saqib Qamar, LinLin Shen, Adnan Saeed. 207-218 [doi]
- Brain Tumor Segmentation Network Using Attention-Based Fusion and Spatial Relationship ConstraintChenyu Liu, Wangbin Ding, Lei Li 0020, Zhen Zhang, Chenhao Pei, Liqin Huang, Xiahai Zhuang. 219-229 [doi]
- Modality-Pairing Learning for Brain Tumor SegmentationYixin Wang, Yao Zhang, Feng Hou, Yang Liu, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He. 230-240 [doi]
- Transfer Learning for Brain Tumor SegmentationJonas Wacker, Marcelo Ladeira, José Eduardo Vaz Nascimento. 241-251 [doi]
- Efficient Embedding Network for 3D Brain Tumor SegmentationHicham Messaoudi, Ahror Belaid, Mohamed Lamine Allaoui, Ahcene Zetout, Mohand Saïd Allili, Souhil Tliba, Douraied Ben Salem, Pierre-Henri Conze. 252-262 [doi]
- Segmentation of the Multimodal Brain Tumor Images Used Res-U-NetJindong Sun, Yanjun Peng, Dapeng Li, Yanfei Guo. 263-273 [doi]
- Vox2Vox: 3D-GAN for Brain Tumour SegmentationMarco Domenico Cirillo, David Abramian, Anders Eklund 0002. 274-284 [doi]
- Automatic Brain Tumor Segmentation with Scale Attention NetworkYading Yuan. 285-294 [doi]
- Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival PredictionCarlo Russo, Sidong Liu, Antonio Di Ieva. 295-306 [doi]
- Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and PriorsYannick Suter, Urspeter Knecht, Roland Wiest, Mauricio Reyes 0001. 307-317 [doi]
- Glioma Segmentation Using Encoder-Decoder Network and Survival Prediction Based on Cox AnalysisEnshuai Pang, Wei Shi, Xuan Li, Qiang Wu 0009. 318-326 [doi]
- Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge SolutionThéophraste Henry, Alexandre Carré, Marvin Lerousseau, Théo Estienne, Charlotte Robert, Nikos Paragios, Eric Deutsch. 327-339 [doi]
- Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR ImagesVaanathi Sundaresan, Ludovica Griffanti, Mark Jenkinson. 340-353 [doi]
- MRI Brain Tumor Segmentation Using a 2D-3D U-Net EnsembleJaime Marti Asenjo, Alfonso Martinez-Larraz Solís. 354-366 [doi]
- Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-ensemble ResUNetLinmin Pei, A. K. Murat, Rivka Colen. 367-375 [doi]
- MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet ArchitecturesLaura Mora Ballestar, Verónica Vilaplana. 376-390 [doi]
- Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival PredictionYue Zhang, Jiewei Wu, WeiKai Huang, Yifan Chen, Ed. X. Wu, Xiaoying Tang. 391-400 [doi]
- Uncertainty-Driven Refinement of Tumor-Core Segmentation Using 3D-to-2D Networks with Label UncertaintyRichard McKinley, Michael Rebsamen, Katrin Daetwyler, Raphael Meier, Piotr Radojewski, Roland Wiest. 401-411 [doi]
- Multi-decoder Networks with Multi-denoising Inputs for Tumor SegmentationMinh H. Vu, Tufve Nyholm, Tommy Löfstedt. 412-423 [doi]
- MultiATTUNet: Brain Tumor Segmentation and Survival MultitaskingDiedre Carmo, Letícia Rittner, Roberto de Alencar Lotufo. 424-434 [doi]
- A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor SegmentationChenggang Lyu, Hai Shu. 435-447 [doi]
- Multidimensional and Multiresolution Ensemble Networks for Brain Tumor SegmentationGowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. 448-457 [doi]
- Cascaded Coarse-to-Fine Neural Network for Brain Tumor SegmentationShuojue Yang, Dong Guo, Lu Wang, Guotai Wang. 458-469 [doi]
- Automated Brain Tumour Segmentation Using Cascaded 3D Densely-Connected U-NetMina Ghaffari, Arcot Sowmya, Ruth Oliver. 481-491 [doi]
- Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival PredictionGuojing Zhao, Bowen Jiang, Jianpeng Zhang, Yong Xia. 492-502 [doi]
- Enhancing MRI Brain Tumor Segmentation with an Additional Classification NetworkHieu T. Nguyen, Tung T. Le, Thang V. Nguyen, Nhan T. Nguyen. 503-513 [doi]
- Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival PredictionChengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai. 514-523 [doi]