The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine（印第安纳大学医学院计算病理学系）；Department of Electrical and Systems Engineering, Washington University in St. Louis（圣路易斯华盛顿大学电气与系统工程系）；GE HealthCare（GE医疗）；Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA), University of Penn（宾夕法尼亚大学综合诊断人工智能与数据科学中心及生物医学图像计算与分析中心）
Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. Quantitative evaluation and ranking were based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency analysis, and test-time instance optimization per-case basis as a post-processing step. The top-ranked method attained the MEE at or below that of the inter-rater variability for approximately 60% of the evaluated landmarks, underscoring the scope for further accuracy and robustness improvements, especially relative to human experts. The aim of BraTS-Reg is to continue to serve as an active resource for research, with the data and online evaluation tools accessible at https://bratsreg.github.io/.
Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data
Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫·扎耶德儿科外科创新研究所）；Children’s National Hospital（儿童国家医院）；Biomedical Image Technologies（生物医学影像技术中心）；Universidad Politécnica de Madrid（马德里理工大学）；CIBER-BBN（西班牙生物医学网络研究中心）；ISCIII（卡洛斯三世卫生研究所）；School of Medicine and Health Sciences, George Washington University（乔治·华盛顿大学医学与健康科学学院）
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）
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）
Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This paper describes our contribution to the Registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends the iRegNet. In particular, incorporating an unsupervised learning-based paradigm as well as several minor modifications to the network pipeline, allows the enhanced iRegNet method to achi…
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma Dataset
Nepal Research and Collaboration Center (NRCC)（尼泊尔研究与合作中心）；Birendra Multiple Campus, Tribhuvan University（特里布万大学比伦德拉多校区）；Institute of Engineering, Purbanchal Campus, Tribhuvan University（特里布万大学普尔班查尔校区工程学院）；Bhaktapur Multiple Campus, Tribhuvan University（特里布万大学巴克塔普尔多校区）；Gandaki College of Engineering and Science, Pokhara University（博卡拉大学甘达基工程与科学学院）；McGill University（麦吉尔大学）；Lawson Health Research Institute（劳森健康研究所）；Medical Artificial Intelligence Laboratory (MAI Lab), Lagos（拉各斯医学人工智能实验室）；University of British Columbia（不列颠哥伦比亚大学）；Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所）
This paper discusses the deep learning architectures currently used for pixel-wise segmentation of primary and secondary glioblastomas and low-grade gliomas. We implement various models such as the popular UNet architecture and compare the performance of these implementations on the BRATS dataset. This paper will explore the different approaches and combinations, offering an in depth discussion of how they perform and how we may improve upon them using more recent advancements in deep learning architectures.
Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging
Tehran University of Medical Science（德黑兰医科大学）；Guilan University of Medical Sciences（吉兰医科大学）；University of Tehran（德黑兰大学）；Motamed Cancer Institute, ACECR（莫塔梅德癌症研究所，学术中心教育文化研究委员会）；University of British Columbia（不列颠哥伦比亚大学）；Technological Virtual Collaboration Company (TECVICO CORP.)（技术虚拟协作公司）；BC Cancer Research Institute（不列颠哥伦比亚省癌症研究所）
Faculty of Physics and Mathematics, Benemérita Universidad Autónoma de Puebla（普埃布拉自治大学物理与数学学院）
Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of conv…
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 …
Structural magnetic resonance imaging (MRI) has been widely utilized for analysis and diagnosis of brain diseases. Automatic segmentation of brain tumors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input sliding patches. Precisely, we first extract 3D patches from each modality to calibrate slices through the squeeze and excitation (SE) block. Then, the outp…
Lausanne University Hospital (CHUV)（洛桑大学医院）；University of Lausanne (Unil)（洛桑大学）；CIBM, Center for Biomedical Imaging（生物医学影像中心）；Neuroscience Research Center（神经科学研究中心）；Lundin Brain Tumor Research Center（Lundin脑肿瘤研究中心）
Gliomas are the most frequent primary brain tumors in adults. Glioma change detection aims at finding the relevant parts of the image that change over time. Although Deep Learning (DL) shows promising performances in similar change detection tasks, the creation of large annotated datasets represents a major bottleneck for supervised DL applications in radiology. To overcome this, we propose a combined use of weak labels (imprecise, but fast-to-create annotations) and Transfer Learning (TL). Specifically, we explore inductive TL, where source and target domains are identical, but tasks are diff…
Washington University in St. Louis（圣路易斯华盛顿大学）；Mallinckrodt Institute of Radiology（马林克罗特放射研究所）；Washington University School of Medicine（华盛顿大学医学院）；Institute for Informatics（信息学研究所）
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are important prognostic markers for glioma. Currently, they are determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to non-invasively determine these molecular alterations from MRI. For this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade II-IV) were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy…
Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis
The Chinese University of Hong Kong (CUHK)（香港中文大学）；CUHK Shenzhen Research Institute（香港中文大学深圳研究院）；China Medical University（中国医科大学）；University College London（伦敦大学学院）；UCL Hawkes Institute（伦敦大学学院霍克斯研究所）
Department of Computer Science, University of Rochester（罗切斯特大学计算机科学系）；Department of Electrical Engineering, University of Rochester（罗切斯特大学电气工程系）；Department of Biomedical Engineering, University of Rochester（罗切斯特大学生物医学工程系）；Department of Imaging Sciences, University of Rochester（罗切斯特大学影像科学系）；Faculty of Medicine and Institute of Clinical Radiology, Ludwig Maximilian University（路德维希-马克西米利安大学医学院及临床放射学研究所）
Glioma is one of the most common and aggressive types of primary brain tumors. The accurate segmentation of subcortical brain structures is crucial to the study of gliomas in that it helps the monitoring of the progression of gliomas and aids the evaluation of treatment outcomes. However, the large amount of required human labor makes it difficult to obtain the manually segmented Magnetic Resonance Imaging (MRI) data, limiting the use of precise quantitative measurements in the clinical practice. In this work, we try to address this problem by developing a 3D Convolutional Neural Network~(3D C…
University of Pennsylvania（宾夕法尼亚大学）；University of California San Francisco（加利福尼亚大学旧金山分校）；University of Toronto（多伦多大学）；Harvard Medical School（哈佛医学院）；Thomas Jefferson University（托马斯·杰斐逊大学）；Universidade Federal de São Paulo（圣保罗联邦大学）；Diagnósticos da América SA（美洲诊断公司）；Technical University of Munich（慕尼黑工业大学）；The Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）；Sage Bionetworks；Kaggle；Leidos Biomedical Research, Inc.（Leidos 生物医学研究公司）
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society. Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging (mpMRI) data. Gliomas are the most common primary malignancies of the central nervous system, with varying degrees of aggressi…
Training Beyond Convergence: Grokking nnU-Net for Glioma Segmentation in Sub-Saharan MRI
Multimedia University（多媒体大学）；University of British Columbia（不列颠哥伦比亚大学）；McGill University（麦吉尔大学）；Montreal Neurological Institute（蒙特利尔神经学研究所）；Federal University of Technology Minna（明纳联邦理工大学）；Medical Artificial Intelligence Laboratory（医学人工智能实验室）；Friedrich-Alexander-Universität（弗里德里希-亚历山大大学）
Resource-Efficient Glioma Segmentation on Sub-Saharan MRI
BAC School of Computing；Information Systems, Botswana Faculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia Laboratory of Biophysics；Medical Technologies, Higher Institute of Medical Technologies of Tunis, University ElManar, Tunis, Tunisia London School of Hygiene；Tropical Medicine, London, UK Lawson Health Research Institute, London, Ontario, Canada Department of Radiology, Bayero University, Kano-Nigeria Department of Radiology, Aminu Kano Teaching Hospital, Kano-Nigeria Department of Anatomy, Federal University Lokoja college of health sciences, Kogi State, Nigeria Department of Computer Science；Information, College of Science, Majmaah University, AL-Majmaah, 11952, Saudi Arabia CES Laboratory, National School of Engineers of Sfax, Sfax, Tunisia Department of Computer Science；IT, Mulungushi University, Zambia Multimodal Imaging of Neurodegenerative Diseases (MiND) Lab, Department of Neurology；Neurosurgery, McGill University, Montreal, QC, Canada Department of Electrical；Computer Engineering, University of British Columbia, Vancouver, Canada Medical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria Montreal Neurological Institute, McGill University, Montréal, Canada Department of Medicine, University of Cape Town, South Africa Department of Clinical \& Radiation Oncology, University of Cape Town, South Africa
The Hong Kong University of Science and Technology（香港科技大学）
Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Lapla…
University of Tehran（德黑兰大学）；University of Nottingham（诺丁汉大学）
In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm based on random forests, for survival prediction is presented. The objective is to segment the glioma area and produce segmentation labels for its different sub-regions, i.e. necrotic and the non-enhancing tumor core, the peri-tumoral edema, and enhancing tumor. The proposed deep architecture for the segmentation task encompasses two parallel streamlines with two different reso-lutions. One deep convolutional neural net…
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…
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）
Deformable image registration is a key task in medical image analysis. The Brain Tumor Sequence Registration challenge (BraTS-Reg) aims at establishing correspondences between pre-operative and follow-up scans of the same patient diagnosed with an adult brain diffuse high-grade glioma and intends to address the challenging task of registering longitudinal data with major tissue appearance changes. In this work, we proposed a two-stage cascaded network based on the Inception and TransMorph models. The dataset for each patient was comprised of a native pre-contrast (T1), a contrast-enhanced T1-w…
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）
Glioma is a common type of brain tumor, and accurate detection of it plays a vital role in the diagnosis and treatment process. Despite advances in medical image analyzing, accurate tumor segmentation in brain magnetic resonance (MR) images remains a challenge due to variations in tumor texture, position, and shape. In this paper, we propose a fast, automated method, with light computational complexity, to find the smallest bounding box around the tumor region. This region-of-interest can be used as a preprocessing step in training networks for subregion tumor segmentation. By adopting the out…
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（Vector研究所）
Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline. However, training accurate and reliable CNNs requires large fine-grain annotated datasets. To alleviate this, weakly-supervised methods can be used to obtain local information from global labels. This work proposes the use of localized perturbations as a weakly-supervised solution to extract segmentation masks of brain tumours from a pretrained 3D classification model. Furthermore, we propose a novel optimal perturbation method that exploits 3D superpixels to…
A Comparison and Evaluation of Fine-tuned Convolutional Neural Networks to Large Language Models for Image Classification and Segmentation of Brain Tumors on MRI
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines
The Hospital for Sick Children (SickKids)（SickKids医院（病童医院））；SickKids Research Institute（SickKids研究所）；University of Toronto（多伦多大学）；University Hospital Augsburg（奥格斯堡大学医院）；Institute of Medical Science, University of Toronto（多伦多大学医学科学研究所）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Mechanical and Industrial Engineering, University of Toronto（多伦多大学机械与工业工程系）；Vector Institute（向量研究院）
impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction
Center for Applied AI, Skolkovo Institute of Science and Technology, Moscow, Russian Federation（应用人工智能中心，斯克尔科夫科学与技术研究所，莫斯科，俄罗斯联邦）；BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory, University of Sharjah, Sharjah, United Arab Emirates（BIMAI实验室，生物医学导向人工智能实验室，肖贾联合大学，肖贾，阿拉伯联合酋长国）；Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russian Federation（伊万诺夫俄罗斯科学院系统编程研究所，莫斯科，俄罗斯联邦）
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute for Systems and Robotics, Instituto Superior Tecnico（高等理工学院系统与机器人研究所）
Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to…
Shanghai University（上海大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）
A glioma is a malignant brain tumor that seriously affects cognitive functions and lowers patients' life quality. Segmentation of brain glioma is challenging because of interclass ambiguities in tumor regions. Recently, deep learning approaches have achieved outstanding performance in the automatic segmentation of brain glioma. However, existing algorithms fail to exploit channel-wise feature interdependence to select semantic attributes for glioma segmentation. In this study, we implement a novel deep neural network that integrates residual channel attention modules to calibrate intermediate …
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）
The Federated Tumor Segmentation and Classification (FeTS++) Challenge
Indiana University School of Medicine（印第安纳大学医学院）；Indiana University Melvin and Bren Simon Comprehensive Cancer Center（印第安纳大学梅尔文与布伦·西蒙综合癌症中心）；Sage Bionetworks（赛奇生物网络公司）；MLCommons（机器学习通用任务组织）；NVIDIA（英伟达公司）；Indiana University（印第安纳大学）
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Center for Data-Driven Discovery in Biomedicine (D3b), Children’s Hospital of Philadelphia（费城儿童医院生物医学数据驱动发现中心）；University of Pennsylvania（宾夕法尼亚大学）；Center for AI & Data Science for Integrated Diagnostics (AI2D)（综合诊断人工智能与数据科学中心）；Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）；Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（儿童医院谢赫·扎耶德儿科外科创新研究所）；Thomas Jefferson University Hospital（托马斯杰斐逊大学医院）；Sage Bionetworks；Montreal Neurological Institute (MNI), McGill University（麦吉尔大学蒙特利尔神经学研究所）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Southern California（南加州大学）；Duke University Medical Center（杜克大学医学中心）；Mayo Clinic（梅奥诊所）；Center for Global Health, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院全球健康中心）；Technical University of Munich（慕尼黑工业大学）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学转化肿瘤学研究中心（TranslaTUM））；National Cancer Institute（美国国家癌症 Institute）
A New Logic For Pediatric Brain Tumor Segmentation
1 Northwestern University, Feinberg School of Medicine, Chicago IL, USA 2 Children’s National Hospital, Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington, DC, USA 3 Ann \& Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA
University Institute of Diagnostic and Interventional Neuroradiology（诊断与介入神经放射学大学研究所）；Inselspital, Bern University Hospital（伯尔尼大学医院 Inselspital）
The BraTS dataset contains a mixture of high-grade and low-grade gliomas, which have a rather different appearance: previous studies have shown that performance can be improved by separated training on low-grade gliomas (LGGs) and high-grade gliomas (HGGs), but in practice this information is not available at test time to decide which model to use. By contrast with HGGs, LGGs often present no sharp boundary between the tumor core and the surrounding edema, but rather a gradual reduction of tumor-cell density. Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, which ranked highl…
Indian Statistical Institute（印度统计研究所）；DAV Institute of Engineering and Technology（DAV工程技术学院）
Inspired by the success of Convolutional Neural Networks (CNN), we develop a novel Computer Aided Detection (CADe) system using CNN for Glioblastoma Multiforme (GBM) detection and segmentation from multi channel MRI data. A two-stage approach first identifies the presence of GBM. This is followed by a GBM localization in each "abnormal" MR slice. As part of the CADe system, two CNN architectures viz. Classification CNN (C-CNN) and Detection CNN (D-CNN) are employed. The CADe system considers MRI data consisting of four sequences ($T_1$, $T_{1c},$ $T_2$, and $T_{2FLAIR}$) as input, and automati…
Shanghai United Imaging Intelligence Co. Ltd（上海联影智能医疗科技有限公司）；Southern Medical University（南方医科大学）
In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network. Then, a standard normal brain atlas image is registered onto such …
National University of Singapore（新加坡国立大学）；National Institute of Technology, Tiruchirappalli（蒂鲁吉拉伯利国立理工学院）
Segmentation of brain tumor from magnetic resonance imaging (MRI) is a vital process to improve diagnosis, treatment planning and to study the difference between subjects with tumor and healthy subjects. In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue. Hypercolumn is the concatenation of a set of vectors which form by extracting convolutional features from multiple layers. Proposed model integrates batch normalization (BN) approach with hypercolumn. BN layers help to alleviate the internal covariate shift duri…
Duke Kunshan University（昆山杜克大学）；Duke University（杜克大学）
We developed a deep ensemble learning model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). This model was developed using 369 glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: fifty-six radiomic features were extracted within the kernel, resulting in a 4th order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map…
