In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
Brain tumour segmentation using a triplanar ensemble of U-Nets
University of Oxford（牛津大学）；Australian Institute for Machine Learning（澳大利亚机器学习研究所）；The University of Adelaide（阿德莱德大学）；South Australian Health and Medical Research Institute（南澳大利亚健康与医学研究所）
Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
Otto von Guericke University Magdeburg（奥托·冯·格里克马格德堡大学）；Center for Behavioral Brain Sciences（行为脑科学中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Leibniz Institute for Neurobiology（莱布尼茨神经生物学研究所）
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
University of Minho（米尼奥大学）；Institute for Artificial Intelligence in Medicine（人工智能医学研究所）；Graz University of Technology（格拉茨工业大学）；University Medicine Essen（埃森大学医学中心）；Cancer Research Center Cologne Essen (CCCE)（科隆-埃森癌症研究中心）；University Hospital Essen（埃森大学医院）；German Cancer Consortium (DKTK)（德国癌症联盟）
Pre- to Post-Contrast Breast MRI Synthesis for Enhanced Tumour Segmentation
Universitat de Barcelona（巴塞罗那大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Karolinska Institutet（卡罗林斯卡学院）；King’s College London（伦敦国王学院）
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
Technische Universität München（慕尼黑工业大学）；Helmholtz Zentrum München（亥姆霍兹慕尼黑研究中心）；TranslaTUM Center for Translational Cancer Research（TranslaTUM转化癌症研究中心）；University of Zürich（苏黎世大学）
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Institute for Cancer Genetics and Informatics（癌症基因组学与信息学研究所）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Department of Gastrointestinal（胃肠科部）；Institute of Clinical Medicine（临床医学研究所）；Department of Gynaecological Oncology（妇科肿瘤科部）；Amsterdam University Medical Centres（阿姆斯特丹大学医学中心）；Department of Obstetrics and Gynaecology（妇产科部）；Comprehensive Cancer Center Innsbruck（因斯布鲁克综合癌症中心）；Innsbruck Medical University（因斯布鲁克医学院）；Vestre Viken Hospital Trust（维斯特维肯医院信托）；Department of Medical Biology（医学生物学部）；UiT The Arctic University of Norway（挪威北极大学（UiT））；Department of Clinical Pathology（临床病理学部）
Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention
University of Oxford（牛津大学）；GE Healthcare（通用电气医疗）
头颈部肿瘤的分割和患者预后的预测对于患者的疾病诊断和治疗监测是至关重要的。目前，强大的深度学习模型的发展受到缺乏具有高质量注释的大型多中心、多模态数据的阻碍。MICCAI 2021 HEad and neCK TumOR（HECKTOR）分割和结果预测挑战创建了一个平台，用于比较氟脱氧葡萄糖（FDG）-PET和计算机断层扫描图像上的主要大体目标体积的分割方法以及H\&N口咽癌无进展生存率的预测。我们提出了一种基于编码器-解码器体系结构的新网络，该体系结构具有完全的帧间和帧内跳过连接，以充分利用低级别和高级别语义。此外，我们使用条件随机场作为后处理步骤来细化预测的分割图。我们训练了多个神经网络进行肿瘤体积分割，这些分割在交叉验证中实现了0.75的平均Dice相似系数，在挑战测试数据集上实现了0.76。为了预测患者无进展生存任务，我们提出了一种结合临床、放射组学和深度学习特征的Cox比例风险回归。我们的生存预测模型在交叉验证中达到了0.82的一致性指数，在挑战测试数据集上达到了0.62。
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany；Biomedical Magnetic Resonance, Faculty of Nature Sciences, Otto von Guericke University Magdeburg, Germany；Genomics Research Centre, Human Technopole, Milan, Italy；Institute for Medical Engineering, Faculty of Electrical Engineering；Information Technology, Otto von Guericke University Magdeburg, Germany；Digital Engineering Faculty, University of Potsdam, Germany；Machine Learning, Hasso-Plattner-Institute, Potsdam, Germany；Department of Biomedical Data Science, Stanford University, Stanford, CA, United States；Centre for Behavioural Brain Sciences, Magdeburg, Germany；German Centre for Neurodegenerative Disease, Magdeburg, Germany
Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study
Queen Square Institute of Neurology, University College London, London, UK（伦敦大学学院医院神经科学研究所）；National Hospital for Neurology and Neurosurgery, London, UK（伦敦神经病学与神经外科医院）；NVIDIA, UK（英国NVIDIA公司）；Great Ormond Street Hospital for Children, London, UK（伦敦儿童医院）；Royal National Orthopaedic Hospital, Stanmore, Middlesex, UK（斯坦莫尔皇家骨科医院，中西敏，英国）；University College Hospitals NHS Foundation Trust, London, UK（伦敦大学学院医院 NHS 基础信托）；Royal Free Hospital, London, UK（伦敦皇家自由医院）；Imperial College Healthcare NHS Trust, London, UK（伦敦帝国学院医疗信托）；Imperial College London, London, UK（伦敦帝国学院）
3-D PET Image Generation with tumour masks using TGAN
University of British Columbia Data Science Institute（不列颠哥伦比亚大学数据科学研究所）；BC Cancer Research Institute（不列颠哥伦比亚省癌症研究所）；University of British Columbia（不列颠哥伦比亚大学）
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Center for Behavioral Brain Sciences（行为脑科学研究中心）；German Center for Neurodegenerative Disease（德国神经退行性疾病研究中心）
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
Institute of Technical；Business Information Systems, Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany Human Technopole, Milan, Italy Institute of Medical Engineering, Faculty of Electrical Engineering；Information Technology, Otto von Guericke University Magdeburg, Magdeburg, Germany Centre for Behavioural Brain Sciences, Magdeburg, Germany
Deep Learning Based Brain Tumor Segmentation: A Survey
University of Leicester（莱斯特大学）；University of Lancaster（兰卡斯特大学）；Queen Mary, University of London（伦敦大学玛丽女王学院）；Xidian University（西安电子科技大学）；Bielefeld University（比勒费尔德大学）
