H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Northwestern Polytechnical University（西北工业大学）；University of Pittsburgh（匹兹堡大学）；University of Sydney（悉尼大学）；JD Finance America Corporation（京东金融美国公司）
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that the combination of the single and cascaded models achieved average Dice scores of 0.78751, 0.91290, and 0.85461, as well as Hausdorff distances ($95\%$) of 26.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor core, respectively. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.
Brain tumor segmentation is a critical task for patient's disease management. In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. Two independent ensembles of models from two different training pipelines were trained, and each produced a brain tumor segmentation map. These two labelmaps per patient were then merged, taking into account the performance of each ensemble for specific tumor subregions. Our perf…
This paper proposes an adversarial learning based training approach for brain tumor segmentation task. In this concept, the 3D segmentation network learns from dual reciprocal adversarial learning approaches. To enhance the generalization across the segmentation predictions and to make the segmentation network robust, we adhere to the Virtual Adversarial Training approach by generating more adversarial examples via adding some noise on original patient data. By incorporating a critic that acts as a quantitative subjective referee, the segmentation network learns from the uncertainty informatio…
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University Hospital（海德堡大学医院）
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the…
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
Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
Institute of Natural Sciences, School of Mathematical Sciences, MOE-LSC, Shanghai Jiao Tong University（自然科学院、数学科学学院、MOE-LSC、上海交通大学）；Department of Radiology, Huashan Hospital, Fudan University（放射科、华山医院、复旦大学）；Department of Neurosurgery, Huashan Hospital, Fudan University（神经外科、华山医院、复旦大学）
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in medical image segmentation tasks. A common feature in most top-performing CNNs is an encoder-decoder architecture inspired by the U-Net. For multi-region brain tumor segmentation, 3D U-Net architecture and its variants provide the most competitive segmentation performances. In this work, we propose an interesting extension of the standard 3D U-Net architecture, specialized for brain tumor segmentation. The proposed network, called E1D3 U-Net, is a one-encoder, three-decoder fully-convolutional neural network…
Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maintained an encoder-decoder based segmentation network, but focused on a modification of network training process that minimizes redundancy under perturb…
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…
Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data
Manning College of Information and Computer Sciences, University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校曼宁信息与计算机科学学院）；Baystate Medical Center（贝斯州医疗中心）；UMass Chan Medical School - Baystate Campus（马萨诸塞大学陈医学院贝斯州校区）
本文面向撒哈拉以南非洲成人患者，提出融合 UNet3D、ONet3D、SphereNet3D 及改进损失函数的十一模型集成脑肿瘤分割方法，在 BraTS AFRICA 数据上取得优于单模型的分割性能。
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Chongqing Engineering Research Center of Software Quality Assurance, Testing and Assessment（重庆市软件质量保证与测评工程技术研究中心）
Vingroup Big Data Institute (VinBigdata)（越南 VinBigdata 大数据研究院）；Hanoi University of Science and Technology (HUST)（河内科技大学）；University of Engineering and Technology (UET), VNU（越南国立大学工程技术大学）
Brain tumor segmentation plays an essential role in medical image analysis. In recent studies, deep convolution neural networks (DCNNs) are extremely powerful to tackle tumor segmentation tasks. We propose in this paper a novel training method that enhances the segmentation results by adding an additional classification branch to the network. The whole network was trained end-to-end on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. On the BraTS's validation set, it achieved an average Dice score of 78.43%, 89.99%, and 84.22% respectively for the enhancing tumo…
Indian Institute of Technology Madras（印度理工学院马德拉斯分校）；Intel Technology India Pvt. Ltd（英特尔技术印度私人有限公司）
We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of weights and residual connections and is initialized with weights obtained from training this model with BraTS 2018 dataset. Hard mining is done during training to train for the difficult cases of segmentation tasks by increasing the dice similarity coefficient (DSC) threshold to choose the hard cases as epoch increases. On the BraTS2020 validation data (n = 125), this architect…
Automatic segmentation of brain tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor Segmentation Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2) Prediction of patient overall survival (OS) from pre-operative MRI scans; 3) Distinction of true tumor recurrence from treatment related effects …
Gliomas are among the most aggressive and deadly brain tumors. This paper details the proposed Deep Neural Network architecture for brain tumor segmentation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inputs. The neuroimaging data are part of the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge dataset, where we evaluated our proposal in the BraTS 2020 Validation and Tes…
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Center for Data-Driven Discovery in Biomedicine (D3b)（生物医学数据驱动发现中心）；Children’s Hospital of Philadelphia（费城儿童医院）；Department of Neurosurgery, University of Pennsylvania（宾夕法尼亚大学神经外科）；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)（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）；Sage Bionetworks（赛基生物网络公司）；Medical Artificial Intelligence (MAI) Lab, Crestview Radiology（Crestview放射科医学人工智能实验室）；Montreal Neurological Institute (MNI), McGill University（麦吉尔大学蒙特利尔神经学研究所）；Department of Radiology, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院放射科）；Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine（印第安纳大学医学院病理学与实验医学系计算病理学分部）；Department of Neurosurgery at the University of Southern California（南加州大学神经外科）；Department of Radiology, Duke University Medical Center（杜克大学医学中心放射科）；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）；Department of Informatics, Technical University Munich（慕尼黑工业大学信息学系）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学TranslaTUM转化癌症研究中心）
Training a deep neural network is an optimization problem with four main ingredients: the design of the deep neural network, the per-sample loss function, the population loss function, and the optimizer. However, methods developed to compete in recent BraTS challenges tend to focus only on the design of deep neural network architectures, while paying less attention to the three other aspects. In this paper, we experimented with adopting the opposite approach. We stuck to a generic and state-of-the-art 3D U-Net architecture and experimented with a non-standard per-sample loss function, the gene…
How We Won BraTS-SSA 2025: Brain Tumor Segmentation in the Sub-Saharan African Population Using Segmentation-Aware Data Augmentation and Model Ensembling
Kwame Nkrumah University of Science and Technology（卡布雷·克鲁马大学科学与技术大学）；University of Cape Coast（科克斯特大学）；University of Toronto（多伦多大学）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）；Department of Physics, Federal University of Technology（联邦技术大学物理系）
The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good processing quality and reliability are the must. Moreover, for wide applications of such systems, a minimization of computation complexity is desirable, which can also result in a minimization of randomness in computation and, consequently, a better performance co…
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）
Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude i…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Center for Data-Driven Discovery in Biomedicine (D3b)（数据驱动生物医学发现中心）；The Children’s Hospital of Philadelphia（费城儿童医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎伊德儿童外科创新研究所）；Children’s National Hospital（儿童国家医院）；Department of Neurosurgery（神经外科部门）；Dana-Farber Brigham Cancer Center & Boston Children’s Hospital（达纳-法伯布里弗癌症中心及波士顿儿童医院）；Department of Paediatric Radiology（儿科放射科部门）；University of Utah（犹他大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医学中心）；Department of Radiology（放射科部门）；Division of Neuroradiology, Department of Radiology, Boston Children’s Hospital, Harvard Medical School（神经放射科，放射科，波士顿儿童医院，哈佛医学院）；College of Arts and Sciences, University of Pennsylvania（艺术与科学学院，宾夕法尼亚大学）；Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences（诊断与介入放射科，全印度医学科学院）；Department of Diagnostic & Interventional Imaging, Neuroradiology section, The University of Texas Health at Houston, Texas, USA（诊断与介入成像部门，神经放射科，德克萨斯大学健康科学中心，德克萨斯，美国）；Beth Israel Deaconess Medical Center, Harvard Medical School（贝斯以色列医疗中心，哈佛医学院）；UNIDAD DE PATOLOGIA CLINICA（临床病理部门）
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor subregional segmentation. Variational autoencoder regularization is utilized in both stages to prevent the overfitting issue. The second-stage network adopts attention gates and is trained additionally using an expanded dataset formed by the first-stage outputs. On the BraTS 2020 validation dataset, the proposed method achieves the mean Dice score of 0.9041, 0.8350, and 0.7958, and Hausdorf…
We apply an ensemble of modified TransBTS, nnU-Net, and a combination of both for the segmentation task of the BraTS 2021 challenge. In fact, we change the original architecture of the TransBTS model by adding Squeeze-and-Excitation blocks, an increasing number of CNN layers, replacing positional encoding in Transformer block with a learnable Multilayer Perceptron (MLP) embeddings, which makes Transformer adjustable to any input size during inference. With these modifications, we are able to largely improve TransBTS performance. Inspired by a nnU-Net framework we decided to combine it with our…
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment. In this paper, we propose a multi-step cascaded network which takes the hierarchical topology of the brain tumor substructures into consideration and segments the substructures from coarse to fine .During segmentation, the result of the former step is utilized as the prior information for the next step to guide the finer segmentation process. The whole network is trained in an end-to-end fashion. Besides, to alleviate the gradient vanishing issue and reduce o…
Shenzhen University（深圳大学）；Huazhong University of Science and Technology（华中科技大学）
The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance…
Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation. Tumor segmentation is one of the fundamental vision tasks necessary for diagnosis and treatment planning of the disease. Previous years winning methods were all deep-learning based, thanks to the advent of modern GPUs, which allow fast optimization of deep convolutional neural network architectures. In this work, we explore best practices of 3D semantic segmentation, including conventional encoder-decoder architecture, as well combined loss functio…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Lenovo Research（联想研究院）
Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment of brain tumor. However, previous methods mostly ignore the latent relationship among different modalities. In this work, we propose a novel end-to-end Modality-Pairing learning method for brain tumor segmentation. Paralleled branches are designed to exploit different modality features and a series of layer connections are utilized to capture complex relationships and abundant information among modalities. We also us…
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）
We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge. To find the optimal model architecture and the learning schedule, we have run an extensive ablation study to test: deep supervision loss, Focal loss, decoder attention, drop block, and residual connections. Additionally, we have searched for the optimal depth of the U-Net encoder, number of convolutional channels and post-processing strategy. Our method won the validation phase and took third place in the test phase. We have open-sourced the code to reproduce our BraTS21 submission at the N…
Non-invasive techniques such as magnetic resonance imaging (MRI) are widely employed in brain tumor diagnostics. However, manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task that requires trained expert radiologists. Due to the subjectivity of manual segmentation, there is low inter-rater reliability which can result in diagnostic discrepancies. As the success of many brain tumor treatments depends on early intervention, early detection is paramount. In this context, a fully automated segmentation method for brain tumor segmentation is necessary as an efficient and…
School of Biomedical Engineering（生物医学工程学院）；Universidad Autónoma de Ciudad Juárez（华雷斯自治大学）
The brain is a complex organ controlling cognitive process and physical functions. Tumors in the brain are accelerated cell growths affecting the normal function and processes in the brain. MRI scans provides detailed images of the body being one of the most common tests to diagnose brain tumors. The process of segmentation of brain tumors from magnetic resonance imaging can provide a valuable guide for diagnosis, treatment planning and prediction of results. Here we consider the problem brain tumor segmentation using a Deep learning architecture for use in tumor segmentation. Although the pro…
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）
Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two …
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 …
Beihang University（北京航空航天大学）；Massachusetts General Hospital, Harvard Medical School（哈佛医学院麻省总医院）；Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
In the clinical diagnosis and treatment of brain tumors, manual image reading consumes a lot of energy and time. In recent years, the automatic tumor classification technology based on deep learning has entered people's field of vision. Brain tumors can be divided into primary and secondary intracranial tumors according to their source. However, to our best knowledge, most existing research on brain tumors are limited to primary intracranial tumor images and cannot classify the source of the tumor. In order to solve the task of tumor source type classification, we analyze the existing technolo…
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；McMaster University（麦克马斯特大学）
Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. Diagnosis and evaluation of brain tumors in the early stages increase the probability of successful treatment. However, the complexity and variety of tumors, shapes, and locations make their segmentation and classification complex. In this regard, numerous researchers have proposed brain tumor segmentation and classification methods. This paper presents an approach that simultaneously segments and classifies brain tumors in MRI images using a framework that contains MRI image enhan…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
McGill University（麦吉尔大学）；University of Oxford（牛津大学）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine at the University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；University of Bern（伯尔尼大学）；Bern University Hospital（伯尔尼大学医院）；University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Universidad de los Andes（洛斯安第斯大学）；Imperial College London（帝国理工学院）；Indian Statistical Institute（印度统计研究所）；University of Calcutta（加尔各答大学）；Uppsala University（乌普萨拉大学）；The University of Pittsburgh Medical Center（匹兹堡大学医学中心）；Inserm（法国国家健康与医学研究院）；University of Auckland（奥克兰大学）；Auckland City Hospital（奥克兰市医院）；Massachusetts General Hospital（麻省总医院）；Massachusetts Institute of Technology（麻省理工学院）；EPITA Research and Development Laboratory (LRDE)（EPITA研发实验室）；Old Dominion University（老道明大学）；University of Girona（赫罗纳大学）；University of Tehran（德黑兰大学）；University of Nottingham（诺丁汉大学）；NVIDIA（英伟达）；University of Virginia（弗吉尼亚大学）；Indian Institute of Technology - Jodhpur（印度理工学院焦特布尔分校）
The brain tumor is the most aggressive kind of tumor and can cause low life expectancy if diagnosed at the later stages. Manual identification of brain tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment and thus reduce the chances of survival for the patient. Medical resonance imaging (MRI) is the conventional method used to diagnose brain tumors and their types. This paper attempts to eliminate the manual process from the diagnosis process and use machine learning instead. We proposed the use of pretrained convolutional neural networks (CNN) for the diagnosis and …
Isfahan University of Technology（伊斯法罕理工大学）；Seattle University（西雅图大学）；McMaster University（麦克马斯特大学）
According to the World Health Organization (WHO), cancer is the second leading cause of death worldwide, responsible for over 9.5 million deaths in 2018 alone. Brain tumors count for one out of every four cancer deaths. Therefore, accurate and timely diagnosis of brain tumors will lead to more effective treatments. Physicians classify brain tumors only with biopsy operation by brain surgery, and after diagnosing the type of tumor, a treatment plan is considered for the patient. Automatic systems based on machine learning algorithms can allow physicians to diagnose brain tumors with noninvasive…
University of Electronic Science and Technology of China（电子科技大学）；New Jersey Institute of Technology（新泽西理工学院）
Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmenta…
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）
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）
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to …
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）
Gliomas appear with wide variation in their characteristics both in terms of their appearance and location on brain MR images, which makes robust tumour segmentation highly challenging, and leads to high inter-rater variability even in manual segmentations. In this work, we propose a triplanar ensemble network, with an independent tumour core prediction module, for accurate segmentation of these tumours and their sub-regions. On evaluating our method on the MICCAI Brain Tumor Segmentation (BraTS) challenge validation dataset, for tumour sub-regions, we achieved a Dice similarity coefficient of…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
University of Zurich（苏黎世大学）；Technical University Munich（慕尼黑工业大学）；Klinikum rechts der Isar, Technical University of Munich（慕尼黑工业大学右岸医院）；Mayo Clinic（梅奥诊所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑亥姆霍兹人工智能研究所）；TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich（慕尼黑工业大学转化肿瘤研究中心TranslaTUM）；Children’s National Hospital（全国儿童医院）；George Washington University（乔治华盛顿大学）；Finnish Center for Artificial Intelligence（芬兰人工智能中心）；Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School（哈佛医学院阿西努拉·A·马蒂诺斯生物医学成像中心）；Crestview Radiology（克雷斯特维尤放射科）；University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Yale University（耶鲁大学）；Children’s Hospital of Philadelphia, University of Pennsylvania（宾夕法尼亚大学费城儿童医院）；Duke University Medical Center（杜克大学医学中心）
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…
College of Physics and Information Engineering, Fuzhou University（福州大学物理与信息工程学院）；School of Data Science, Fudan University（复旦大学数据科学学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）
Delineating the brain tumor from magnetic resonance (MR) images is critical for the treatment of gliomas. However, automatic delineation is challenging due to the complex appearance and ambiguous outlines of tumors. Considering that multi-modal MR images can reflect different tumor biological properties, we develop a novel multi-modal tumor segmentation network (MMTSN) to robustly segment brain tumors based on multi-modal MR images. The MMTSN is composed of three sub-branches and a main branch. Specifically, the sub-branches are used to capture different tumor features from multi-modal images,…
Today, over 700,000 people are living with brain tumors in the United States. Brain tumors can spread very quickly to other parts of the brain and the spinal cord unless necessary preventive action is taken. Thus, the survival rate for this disease is less than 40% for both men and women. A conclusive and early diagnosis of a brain tumor could be the difference between life and death for some. However, brain tumor detection and segmentation are tedious and time-consuming processes as it can only be done by radiologists and clinical experts. The use of computer vision techniques, such as Mask R…
McGill University（麦吉尔大学）；University of Oxford（牛津大学）
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 …
