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ORCID5th Brainles@MICCAI 2019: Shenzhen, China - Part I
- Alessandro Crimi
, Spyridon Bakas:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I. Lecture Notes in Computer Science 11992, Springer 2020, ISBN 978-3-030-46639-8
Brain Lesion Image Analysis
- Mohammad Hamghalam, Baiying Lei, Tianfu Wang:
Convolutional 3D to 2D Patch Conversion for Pixel-Wise Glioma Segmentation in MRI Scans. 3-12 - Miguel Monteiro, Konstantinos Kamnitsas, Enzo Ferrante, Francois Mathieu, Steven McDonagh, Sam Cook, Susan Stevenson, Tilak Das, Aneesh Khetani, Tom Newman, Fred Zeiler, Richard Digby, Jonathan P. Coles, Daniel Rueckert, David K. Menon, Virginia F. J. Newcombe, Ben Glocker:
TBI Lesion Segmentation in Head CT: Impact of Preprocessing and Data Augmentation. 13-22 - Mingsong Zhou, Xingce Wang, Zhongke Wu, Jose M. Pozo, Alejandro F. Frangi:
Aneurysm Identification in Cerebral Models with Multiview Convolutional Neural Network. 23-31 - Po-Yu Kao, Jefferson W. Chen, B. S. Manjunath:
Predicting Clinical Outcome of Stroke Patients with Tractographic Feature. 32-43 - Caleb M. Grenko, Angela N. Viaene, MacLean P. Nasrallah, Michael D. Feldman, Hamed Akbari, Spyridon Bakas:
Towards Population-Based Histologic Stain Normalization of Glioblastoma. 44-56 - Siddhesh P. Thakur, Jimit Doshi, Sarthak Pati, Sung Min Ha, Chiharu Sako, Sanjay N. Talbar, Uday Kulkarni, Christos Davatzikos, Güray Erus, Spyridon Bakas:
Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning. 57-68 - Christian Lucas, Linda F. Aulmann, André Kemmling, Amir Madany Mamlouk, Mattias P. Heinrich:
Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomes. 69-79 - Huan Wang, Guotai Wang, Zijian Liu, Shaoting Zhang:
Global and Local Multi-scale Feature Fusion Enhancement for Brain Tumor Segmentation and Pancreas Segmentation. 80-88 - Jeroen Bertels, David Robben, Dirk Vandermeulen, Paul Suetens:
Optimization with Soft Dice Can Lead to a Volumetric Bias. 89-97 - Mattias Billast, Maria Inês Meyer, Diana Maria Sima, David Robben:
Improved Inter-scanner MS Lesion Segmentation by Adversarial Training on Longitudinal Data. 98-107 - Joshua Durso-Finley, Douglas L. Arnold, Tal Arbel:
Saliency Based Deep Neural Network for Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI. 108-118 - Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov, Egor Krivov, Valery Kostjuchenko, Amayak Durgaryan, Mikhail Galkin, Ivan Osinov, Andrey Golanov, Mikhail Belyaev:
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation. 119-128
Brain Tumor Image Segmentation
- Feifan Wang, Runzhou Jiang, Liqin Zheng, Chun Meng, Bharat B. Biswal:
3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction. 131-141 - Minglin Chen, Yaozu Wu, Jianhuang Wu:
Aggregating Multi-scale Prediction Based on 3D U-Net in Brain Tumor Segmentation. 142-152 - Mohammad Hamghalam, Baiying Lei, Tianfu Wang:
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans. 153-162 - Xiangyu Li, Gongning Luo, Kuanquan Wang:
Multi-step Cascaded Networks for Brain Tumor Segmentation. 163-173 - Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks. 174-186 - Nicolas Boutry, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Hugues Talbot, Thierry Géraud:
Using Separated Inputs for Multimodal Brain Tumor Segmentation with 3D U-Net-like Architectures. 187-199 - Soopil Kim, Miguel Luna, Philip Chikontwe, Sang Hyun Park:
Two-Step U-Nets for Brain Tumor Segmentation and Random Forest with Radiomics for Survival Time Prediction. 200-209 - Yuan-Xing Zhao, Yan-Ming Zhang, Cheng-Lin Liu:
Bag of Tricks for 3D MRI Brain Tumor Segmentation. 210-220 - Mehdi Amian, Mohammadreza Soltaninejad:
Multi-resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction. 221-230 - Zeyu Jiang, Changxing Ding, Minfeng Liu, Dacheng Tao:
Two-Stage Cascaded U-Net: 1st Place Solution to BraTS Challenge 2019 Segmentation Task. 231-241 - Xinchao Cheng, Zongkang Jiang, Qiule Sun, Jianxin Zhang:
Memory-Efficient Cascade 3D U-Net for Brain Tumor Segmentation. 242-253 - Florian Kofler, Johannes C. Paetzold, Ivan Ezhov, Suprosanna Shit, Daniel Krahulec, Jan S. Kirschke, Claus Zimmer, Benedikt Wiestler, Bjoern H. Menze:
A Baseline for Predicting Glioblastoma Patient Survival Time with Classical Statistical Models and Primitive Features Ignoring Image Information. 254-261 - Mobarakol Islam, V. S. Vibashan, V. Jeya Maria Jose, Navodini Wijethilake, Uppal Utkarsh, Hongliang Ren:
Brain Tumor Segmentation and Survival Prediction Using 3D Attention UNet. 262-272 - Wei Shi, Enshuai Pang, Qiang Wu, Fengming Lin:
Brain Tumor Segmentation Using Dense Channels 2D U-net and Multiple Feature Extraction Network. 273-283 - M. Subin Sahayam, Nanda H. Krishna, Umarani Jayaraman:
Brain Tumour Segmentation on MRI Images by Voxel Classification Using Neural Networks, and Patient Survival Prediction. 284-294 - Javier Juan-Albarracín, Elies Fuster-García, María del Mar Álvarez-Torres, Eduard Chelebian, Juan M. García-Gómez:
ONCOhabitats Glioma Segmentation Model. 295-303 - Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer:
Brain Tumor Segmentation with Uncertainty Estimation and Overall Survival Prediction. 304-314 - Dong Guo, Lu Wang, Tao Song, Guotai Wang:
Cascaded Global Context Convolutional Neural Network for Brain Tumor Segmentation. 315-326 - Leon Weninger, Qianyu Liu, Dorit Merhof:
Multi-task Learning for Brain Tumor Segmentation. 327-337 - Rupal R. Agravat, Mehul S. Raval:
Brain Tumor Segmentation and Survival Prediction. 338-348 - Sun'ao Liu, Xiaonan Guo:
Improving Brain Tumor Segmentation with Multi-direction Fusion and Fine Class Prediction. 349-358 - Kamlesh Pawar, Zhaolin Chen, N. Jon Shah, Gary F. Egan:
An Ensemble of 2D Convolutional Neural Network for 3D Brain Tumor Segmentation. 359-367 - Sebastian Starke, Carlchristian Eckert, Alex Zwanenburg, Stefanie Speidel, Steffen Löck, Stefan Leger:
An Integrative Analysis of Image Segmentation and Survival of Brain Tumour Patients. 368-378 - Richard McKinley, Michael Rebsamen, Raphael Meier, Roland Wiest:
Triplanar Ensemble of 3D-to-2D CNNs with Label-Uncertainty for Brain Tumor Segmentation. 379-387 - Markus Frey, Matthias Nau:
Memory Efficient Brain Tumor Segmentation Using an Autoencoder-Regularized U-Net. 388-396
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