Table of Contents

• Surface Agnostic Metrics for Cortical Volume Segmentation and RegressionSamuel Budd, Prachi A. Patkee, Ana Baburamani, Mary A. Rutherford, Emma C. Robinson, Bernhard Kainz. 3-12 [doi]
• Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain AnatomyGabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli, Paolo Avesani, Filippo Arrigoni, Emanuele Olivetti. 13-22 [doi]
• Bidirectional Modeling and Analysis of Brain Aging with Normalizing FlowsMatthias Wilms, Jordan J. Bannister, Pauline Mouches, M. Ethan MacDonald, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert. 23-33 [doi]
• A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional ConnectivityNaresh Nandakumar, Niharika Shimona D'Souza, Komal Manzoor, Jay J. Pillai, Sachin K. Gujar, Haris I. Sair, Archana Venkataraman. 34-44 [doi]
• Deep Learning for Non-invasive Cortical Potential ImagingAlexandra Razorenova, Nikolay B. Yavich, Mikhail S. Malovichko, Maxim Fedorov, Nikolay A. Koshev, Dmitry V. Dylov. 45-55 [doi]
• An Anatomically-Informed 3D CNN for Brain Aneurysm Classification with Weak LabelsTommaso Di Noto, Guillaume Marie, Sébastien Tourbier, Yasser Alemán-Gómez, Guillaume Saliou, Meritxell Bach Cuadra, Patric Hagmann, Jonas Richiardi. 56-66 [doi]
• Ischemic Stroke Segmentation from CT Perfusion Scans Using Cluster-Representation LearningJianyuan Zhang, Feng Shi 0001, Lei Chen, Zhong Xue, Lichi Zhang, Dahong Qian. 67-76 [doi]
• SeizureNet: Multi-Spectral Deep Feature Learning for Seizure Type ClassificationUmar Asif, Subhrajit Roy, Jianbin Tang, Stefan Harrer. 77-87 [doi]
• Decoding Task States by Spotting Salient Patterns at Time Points and Brain RegionsYi Hao Chan, Sukrit Gupta, L. L. Chamara Kasun, Jagath C. Rajapakse. 88-97 [doi]
• Patch-Based Brain Age Estimation from MR ImagesKyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson, Alexander Hammers, Daniel Rueckert. 98-107 [doi]
• Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and Keypoint MaskingÉtienne Pepin, Jean-Baptiste Carluer, Laurent Chauvin, Matthew Toews, Rola Harmouche. 108-118 [doi]
• A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple SclerosisStefano Cerri, Andrew Hoopes, Douglas N. Greve, Mark Mühlau, Koen Van Leemput. 119-128 [doi]
• Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature LearningYannick Suter, Urspeter Knecht, Roland Wiest, Ekkehard Hewer, Philippe Schucht, Mauricio Reyes 0001. 129-138 [doi]
• Generalizing MRI Subcortical Segmentation to NeurodegenerationHao Li, Huahong Zhang, Dewei Hu, Hans J. Johnson, Jeffrey D. Long, Jane S. Paulsen, Ipek Oguz. 139-147 [doi]
• Multiple Sclerosis Lesion Segmentation Using Longitudinal Normalization and Convolutional Recurrent Neural NetworksSergio Tascon-Morales, Stefan Hoffmann, Martin Treiber, Daniel Mensing, Arnau Oliver, Matthias Günther, Johannes Gregori. 148-158 [doi]
• Deep Voxel-Guided Morphometry (VGM): Learning Regional Brain Changes in Serial MRIAlena-Kathrin Schnurr, Philipp Eisele, Christina Rossmanith, Stefan Hoffmann, Johannes Gregori, Andreas Dabringhaus, Matthias Kraemer, Raimar Kern, Achim Gass, Frank G. Zöllner. 159-168 [doi]
• A Deep Transfer Learning Framework for 3D Brain Imaging Based on Optimal Mass TransportLing-Li Zeng, Christopher R. K. Ching, Zvart Abaryan, Sophia I. Thomopoulos, Kai Gao, Alyssa H. Zhu, Anjanibhargavi Ragothaman, Faisal Rashid, Marc Harrison, Lauren E. Salminen, Brandalyn C. Riedel, Neda Jahanshad, Dewen Hu, Paul M. Thompson. 169-176 [doi]
• Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesGuy Leroy, Daniel Rueckert, Amir Alansary. 177-186 [doi]
• State-of-the-Art in Brain Tumor Segmentation and Current ChallengesSobia Yousaf, Harish RaviPrakash, Syed Muhammad Anwar, Nosheen Sohail, Ulas Bagci. 189-198 [doi]
• Radiomics and Radiogenomics with Deep Learning in Neuro-oncologyJay B. Patel, Mishka Gidwani, Ken Chang, Jayashree Kalpathy-Cramer. 199-211 [doi]
• Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021Thomas C. Booth, Bernice Akpinar, Andrei Roman, Haris Shuaib, Aysha Luis, Alysha Chelliah, Ayisha Al Busaidi, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, Keyoumars Ashkan, Sébastien Ourselin, Marc Modat. 212-228 [doi]
• Radiogenomics of Glioblastoma: Identification of Radiomics Associated with Molecular SubtypesNavodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Hongliang Ren 0001. 229-239 [doi]
• Local Binary and Ternary Patterns Based Quantitative Texture Analysis for Assessment of IDH Genotype in Gliomas on Multi-modal MRISonal Gore, Tanay Chougule, Jitender Saini, Madhura Ingalhalikar, Jayant Jagtap. 240-248 [doi]
• Automated Multi-class Brain Tumor Types Detection by Extracting RICA Based Features and Employing Machine Learning TechniquesSadia Anjum, Lal Hussain, Mushtaq Ali, Adeel Ahmed Abbasi. 249-258 [doi]
• Overall Survival Prediction in Gliomas Using Region-Specific Radiomic FeaturesAsma Shaheen, Stefano Burigat, Ulas Bagci, Hassan Mohy-ud-Din. 259-267 [doi]
• Using Functional Magnetic Resonance Imaging and Personal Characteristics Features for Detection of Neurological ConditionsBatool Rathore, Muhammad Awais, Muhammad Usama Usman, Imran Shafi, Waqas Ahmed. 268-275 [doi]
• Differentiation of Recurrent Glioblastoma from Radiation Necrosis Using Diffusion Radiomics: Machine Learning Model Development and External ValidationYae Won Park, Ji Eun Park, Sungsoo Ahn, Hwiyoung Kim, Ho Sung Kim, Seung-koo Lee. 276-283 [doi]
• Brain Tumor Survival Prediction Using Radiomics FeaturesSobia Yousaf, Syed Muhammad Anwar, Harish RaviPrakash, Ulas Bagci. 284-293 [doi]
• Brain MRI Classification Using Gradient BoostingMuhammad Tahir. 294-301 [doi]