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ORCID6th Brainles@MICCAI 2020: Lima, Peru - Part I
- Alessandro Crimi
, Spyridon Bakas:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part I. Lecture Notes in Computer Science 12658, Springer 2021, ISBN 978-3-030-72083-4
Invited Papers
- MacLean P. Nasrallah:
Glioma Diagnosis and Classification: Illuminating the Gold Standard. 3-10 - Huahong Zhang, Ipek Oguz:
Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methods. 11-29 - Anahita Fathi Kazerooni, Christos Davatzikos:
Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics. 30-38
Brain Lesion Image Analysis
- Zhongqiang Liu, Dongdong Gu, Yu Zhang, Xiaohuan Cao, Zhong Xue:
Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks. 41-50 - Guoqing Wu, Li Zhang, Xi Chen, Jixian Lin, Yuanyuan Wang, Jinhua Yu:
Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmentation on Computed Tomographic Angiography. 51-59 - Yanlin Liu, Xiangzhu Zeng, Chuyang Ye:
Volume Preserving Brain Lesion Segmentation. 60-69 - Lorenza 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:
Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Primary Progressive Multiple Sclerosis. 70-79 - Xiaofeng Liu, Fangxu Xing, Chao Yang, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo:
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology. 80-91 - Lucas Martin, Julie Josse, Bertrand Thirion:
Multivariate Analysis is Sufficient for Lesion-Behaviour Mapping. 92-100 - Junichiro Iwasawa, Yuichiro Hirano, Yohei Sugawara:
Label-Efficient Multi-task Segmentation Using Contrastive Learning. 101-110 - Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong-Tae Kim, Nassir Navab:
Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation. 111-121 - Hui Yu, Wenjun Xia, Yan Liu, Xuejun Gu, Jiliu Zhou, Yi Zhang:
MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection. 122-132 - Jaime Simarro Viana, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben, Diana Maria Sima:
Unsupervised 3D Brain Anomaly Detection. 133-142 - Tejas Sudharshan Mathai, Yi Wang, Nathan M. Cross:
Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRI. 143-156 - Sarthak Pati, Vaibhav Sharma, Heena Aslam, Siddhesh P. Thakur, Hamed Akbari, Andreas Mang, Shashank Subramanian, George Biros, Christos Davatzikos, Spyridon Bakas:
Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression. 157-167 - Julian Klug, Guillaume Leclerc, Elisabeth Dirren, Maria Giulia Preti, Dimitri Van De Ville, Emmanuel Carrera:
Bayesian Skip Net: Building on Prior Information for the Prediction and Segmentation of Stroke Lesions. 168-180
Brain Tumor Segmentation
- Wen Jun, Haoxiang Xu, Zhang Wang:
Brain Tumor Segmentation Using Dual-Path Attention U-Net in 3D MRI Images. 183-193 - Il Song Han:
Multimodal Brain Image Analysis and Survival Prediction Using Neuromorphic Attention-Based Neural Networks. 194-206 - Parvez Ahmad, Saqib Qamar, Linlin Shen, Adnan Saeed:
Context Aware 3D UNet for Brain Tumor Segmentation. 207-218 - Chenyu Liu, Wangbin Ding, Lei Li, Zhen Zhang, Chenhao Pei, Liqin Huang, Xiahai Zhuang:
Brain Tumor Segmentation Network Using Attention-Based Fusion and Spatial Relationship Constraint. 219-229 - Yixin Wang, Yao Zhang, Feng Hou, Yang Liu, Jiang Tian, Cheng Zhong, Yang Zhang, Zhiqiang He:
Modality-Pairing Learning for Brain Tumor Segmentation. 230-240 - Jonas Wacker, Marcelo Ladeira, José Eduardo Vaz Nascimento:
Transfer Learning for Brain Tumor Segmentation. 241-251 - Hicham Messaoudi, Ahror Belaid, Mohamed Lamine Allaoui, Ahcene Zetout, Mohand Saïd Allili, Souhil Tliba, Douraied Ben Salem, Pierre-Henri Conze:
Efficient Embedding Network for 3D Brain Tumor Segmentation. 252-262 - Jindong Sun, Yanjun Peng, Dapeng Li, Yanfei Guo:
Segmentation of the Multimodal Brain Tumor Images Used Res-U-Net. 263-273 - Marco Domenico Cirillo, David Abramian, Anders Eklund:
Vox2Vox: 3D-GAN for Brain Tumour Segmentation. 274-284 - Yading Yuan:
Automatic Brain Tumor Segmentation with Scale Attention Network. 285-294 - Carlo Russo, Sidong Liu, Antonio Di Ieva:
Impact of Spherical Coordinates Transformation Pre-processing in Deep Convolution Neural Networks for Brain Tumor Segmentation and Survival Prediction. 295-306 - Yannick Suter, Urspeter Knecht, Roland Wiest, Mauricio Reyes:
Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors. 307-317 - Enshuai Pang, Wei Shi, Xuan Li, Qiang Wu:
Glioma Segmentation Using Encoder-Decoder Network and Survival Prediction Based on Cox Analysis. 318-326 - Théophraste Henry, Alexandre Carré, Marvin Lerousseau, Théo Estienne, Charlotte Robert, Nikos Paragios, Eric Deutsch:
Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution. 327-339 - Vaanathi Sundaresan, Ludovica Griffanti, Mark Jenkinson:
Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR Images. 340-353 - Jaime Marti Asenjo, Alfonso Martinez-Larraz Solís:
MRI Brain Tumor Segmentation Using a 2D-3D U-Net Ensemble. 354-366 - Linmin Pei, A. K. Murat, Rivka Colen:
Multimodal Brain Tumor Segmentation and Survival Prediction Using a 3D Self-ensemble ResUNet. 367-375 - Laura Mora Ballestar, Verónica Vilaplana:
MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet Architectures. 376-390 - Yue Zhang, Jiewei Wu, Weikai Huang, Yifan Chen, Ed X. Wu, Xiaoying Tang:
Utility of Brain Parcellation in Enhancing Brain Tumor Segmentation and Survival Prediction. 391-400 - Richard McKinley, Michael Rebsamen, Katrin Daetwyler, Raphael Meier, Piotr Radojewski, Roland Wiest:
Uncertainty-Driven Refinement of Tumor-Core Segmentation Using 3D-to-2D Networks with Label Uncertainty. 401-411 - Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation. 412-423 - Diedre Carmo, Letícia Rittner, Roberto de Alencar Lotufo:
MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking. 424-434 - Chenggang Lyu, Hai Shu:
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation. 435-447 - Gowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian:
Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation. 448-457 - Shuojue Yang, Dong Guo, Lu Wang, Guotai Wang:
Cascaded Coarse-to-Fine Neural Network for Brain Tumor Segmentation. 458-469 - Pooya Ashtari, Frederik Maes, Sabine Van Huffel:
Low-Rank Convolutional Networks for Brain Tumor Segmentation. 470-480 - Mina Ghaffari, Arcot Sowmya, Ruth Oliver:
Automated Brain Tumour Segmentation Using Cascaded 3D Densely-Connected U-Net. 481-491 - Guojing Zhao, Bowen Jiang, Jianpeng Zhang, Yong Xia:
Segmentation then Prediction: A Multi-task Solution to Brain Tumor Segmentation and Survival Prediction. 492-502 - Hieu T. Nguyen, Tung T. Le, Thang V. Nguyen, Nhan T. Nguyen:
Enhancing MRI Brain Tumor Segmentation with an Additional Classification Network. 503-513 - Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai:
Self-training for Brain Tumour Segmentation with Uncertainty Estimation and Biophysics-Guided Survival Prediction. 514-523
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