The optimal treatment strategy of newly diagnosed glioma is strongly influenced by tumour malignancy. Manual non-invasive grading based on MRI is not always accurate and biopsies to verify diagnosis negatively impact overall survival. In this paper, we propose a fully automatic 3D computer-aided diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma from lower-grade glioma. The approach consists of an automatic segmentation step to extract the tumour ROI followed by classification using a 3D convolutional neural network. Segmentation was performed using a 3D U-Net achieving a dice score of 88.53% which matches top performing algorithms in the BraTS 2018 challenge. The classification network was trained and evaluated on a large heterogeneous dataset of 549 patients reaching an accuracy of 91%. Additionally, the CAD system was evaluated on data from the Ghent University Hospital and achieved an accuracy of 92% which shows that the algorithm is robust to data from different centres.
Zhengzhou University（郑州大学）；Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences（广东省人民医院（广东省医学科学院）医学影像人工智能分析应用广东省重点实验室）
Radiomics and deep learning have shown high popularity in automatic glioma grading. Radiomics can extract hand-crafted features that quantitatively describe the expert knowledge of glioma grades, and deep learning is powerful in extracting a large number of high-throughput features that facilitate the final classification. However, the performance of existing methods can still be improved as their complementary strengths have not been sufficiently investigated and integrated. Furthermore, lesion maps are usually needed for the final prediction at the testing phase, which is very troublesome. I…
University of Cambridge（剑桥大学）；Beijing Institute of Technology（北京理工大学）
Whole slide images (WSI) provide valuable phenotypic information for histological assessment and malignancy grading of tumors. The WSI-based grading promises to provide rapid diagnostic support and facilitate digital health. Currently, the most commonly used WSIs are derived from formalin-fixed paraffin-embedded (FFPE) and Frozen section. The majority of automatic tumor grading models are developed based on FFPE sections, which could be affected by the artifacts introduced by tissue processing. The frozen section exists problems such as low quality that might influence training within single m…
In the WHO glioma classification guidelines grade, IDH mutation and 1p19q co-deletion play a central role as they are important markers for prognosis and optimal therapy planning. Therefore, we propose a fully automatic, MRI based, 3D pipeline for glioma segmentation and classification. The designed segmentation network was a 3D U-Net achieving an average whole tumor dice score of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the multi-task classification network. The network was trained and evaluated on a large heterogeneous dataset of 628 patients, collected from The Ca…
Zhengzhou University（郑州大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Peng Cheng Laboratory（鹏城实验室）；Guangdong Provincial People’s Hospital（广东省人民医院）；Guangdong Academy of Medical Sciences（广东省医学科学院）
Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most of the existing relevant studies, in which remarkable diagnosis results have been reported. Nevertheless, acquiring contrast-enhanced MRI data is sometimes not feasible due to the patients physiological limitations. Furthermore, it is more time-consuming and costly to collect contrast-enhanced MRI data in the clinic. In this paper, we propose an adaptive PromptNet to …
Objectives: Glioblastomas are the most aggressive brain and central nervous system (CNS) tumors with poor prognosis in adults. The purpose of this study is to develop a machine-learning based classification method using radio-mic features of multi-parametric MRI to classify high-grade gliomas (HGG) and low-grade gliomas (LGG). Methods: Multi-parametric MRI of 80 patients, 40 HGG and 40 LGG, with gliomas from the MICCAI BRATs 2015 training database were used in this study. Each patient's T1, contrast-enhanced T1, T2, and Fluid Attenuated Inversion Recovery (FLAIR) MRIs as well as the tumor cont…
Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are exclusively based on hand-crafted qualitative or semi-quantitative dynamic features extracted from the mean time activity curve (TAC) within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumour heterogeneity of gliomas. In this work, we investigate the ability of the widely used principal component analysis (PCA) method …
Glioma Multimodal MRI Analysis System for Tumor Layered Diagnosis via Multi-task Semi-supervised Learning
Cancer Research Institute, Central South University（中南大学肿瘤研究所）；School of Physics & Electronic Science, Changsha University of Science & Technology（长沙理工大学物理与电子科学学院）；IFLYTEK Research（iFlytek研究院）；School of Life Sciences, Central South University（中南大学生命科学学院）；Department of Radiology, Xiangya Hospital, Central South University（中南大学湘雅医院放射科）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Department of Neurosurgery, Xiangya Hospital, Central South University（中南大学湘雅医院神经外科）
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）；Hannover Medical School（汉诺威医学院）；Medical Center - University of Freiburg（弗莱堡大学医学中心）；RWTH Aachen University Hospital（亚琛工业大学医院）；Klinikum Bremen-Mitte（不来梅中部医院）；Leibniz Institute for Prevention Research and Epidemiology - BIPS（莱布尼茨预防研究与流行病学研究所）；Bremen Cancer Registry（不来梅癌症登记处）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB（弗劳恩霍夫光电、系统技术及图像处理研究所IOSB）；German Heart Center Berlin（柏林德国心脏中心）
Reutlingen University（罗伊特林根大学）；Menoufia University（梅努菲亚大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, De…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)
Medical Artificial Intelligence Laboratory (MAI Lab)（医学人工智能实验室（MAI实验室））；Department of Radiation Biology, Radiotherapy and Radiodiagnosis, University of Lagos（拉各斯大学放射生物学、放射治疗与放射诊断系）；Department of Radiology, University of California, San Diego（加州大学圣地亚哥分校放射科）；Crestview Radiology Limited（Crestview放射科有限公司）；Lagos University Teaching Hospital（拉各斯大学教学医院）；Lagos State University Teaching Hospital, Ikeja, Lagos, Nigeria（拉各斯州大学教学医院，伊凯贾，拉各斯，尼日利亚）；NSIA-Kano Diagnostic Center, Kano Nigeria（NSIA-卡诺诊断中心，卡诺，尼日利亚）；Nnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria（恩内迪·阿齐基韦大学教学医院，恩韦伊，安纳博拉州，尼日利亚）；Federal Medical Centre, Abeokuta, Ogun State, Nigeria（阿博库塔联邦医疗中心，奥贡州，尼日利亚）；Federal Medical Centre, Umahia, Abia State, Nigeria（乌马希亚联邦医疗中心，阿比亚州，尼日利亚）；National Hospital Abuja, FCT, Nigeria（阿布贾国家医院，联邦首都区，尼日利亚）；Benue State University Teaching Hospital, Markurdi, Benue State, Nigeria（贝努埃州大学教学医院，马库尔迪，贝努埃州，尼日利亚）；Duke University Medical Center, Department of Radiology, USA（达特茅斯大学医学中心，放射科，美国）；University of California San Francisco, CA, USA（加州大学旧金山分校，CA，美国）；Yale University, New Haven, CT, USA（耶鲁大学，新 Haven，CT，美国）；Children's National Hospital, Washington DC, USA（儿童医院华盛顿特区，华盛顿特区，美国）；George Washington University, Washington DC, USA（乔治·华盛顿大学，华盛顿特区，美国）；Sage Bionetworks, USA（Sage生物网络，美国）；Department of Neuroradiology, Technical University of Munich, Munich, Germany（慕尼黑技术大学神经放射科系，慕尼黑，德国）；Helmholtz Research Center, Munich, Germany（海德堡研究中心，慕尼黑，德国）；Duke University Medical Center, Department of Radiation Oncology, USA（达特茅斯大学医学中心，放射肿瘤科，美国）；Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA（费城儿童医院，宾夕法尼亚大学，费城，PA，美国）；Center for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA（人工智能与数据科学整合诊断中心（AI2D）及生物医学影像计算与分析中心（CBICA），宾夕法尼亚大学，费城，PA，美国）；Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA（Athinoula A Martinos生物医学影像中心，马萨诸塞总医院，波士顿，MA，美国）；University of Zurich, Switzerland（苏黎世大学，瑞士）；Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA（癌症成像计划，国家癌症研究所，国家卫生研究院，贝塞斯达，MD 20814，美国）；Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, USA（临床流行病学与生物统计学中心，宾夕法尼亚大学，费城，美国）；Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark（应用数学与计算机科学系，丹麦技术大学，丹麦）；Mayo Clinic, MN, USA（梅奥诊所，MN，美国）；Precision FDA, U.S. Food and Drug Administration, Silver Spring, MD, USA（Precision FDA，美国食品药品监督管理局，Silver Spring，MD，美国）；Booz Allen Hamilton, McLean, VA, USA（Booz Allen Hamilton，麦肯，VA，美国）；Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA（放射科，佩尔曼医学院，宾夕法尼亚大学，费城，PA，美国）；Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA（病理学与实验室医学系，佩尔曼医学院，宾夕法尼亚大学，费城，PA，美国）；Center for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA（全球健康中心，佩尔曼医学院，宾夕法尼亚大学，费城，宾夕法尼亚，美国）；Montreal Neurological Institute, McGill University, Montreal, Canada（蒙特利尔神经科学研究所，麦吉尔大学，蒙特利尔，加拿大）；Department of Medicine, University of Cape Town, South Africa（医学系，开普敦大学，南非）；Department of Radiation Medicine, University of Cape Town, South Africa（放射医学系，开普敦大学，南非）
EURECOM（欧洲通信学院）；University of Brasília（巴西利亚大学）；Syrian-Lebanese Hospital（叙利亚-黎巴嫩医院）
Gliomas are the most common malignant brain tumors that are treated with chemoradiotherapy and surgery. Magnetic Resonance Imaging (MRI) is used by radiotherapists to manually segment brain lesions and to observe their development throughout the therapy. The manual image segmentation process is time-consuming and results tend to vary among different human raters. Therefore, there is a substantial demand for automatic image segmentation algorithms that produce a reliable and accurate segmentation of various brain tissue types. Recent advances in deep learning have led to convolutional neural ne…
Future Processing（Future Processing公司）；Silesian University of Technology（西里西亚工业大学）；Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology（玛丽亚·斯克沃多夫斯卡-居里纪念癌症中心与肿瘤研究所）；Feedback Medical Ltd.（Feedback Medical有限公司）
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumor. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human error. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark (B…
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
