Clinicians diagnose brain tumors by synthesizing patient symptoms, medical history, and quantitative imaging data from modalities such as MRI and CT scans into a unified clinical judgement. However, most deep learning models rely on MRI/CT images alone, failing to replicate the clinicians multimodal reasoning. We explore a two-branch multimodal network combining raw MRI scans with 91 extracted radiomic features (intensity, texture, shape, and boundary descriptors) to classify brain tumors into glioma, meningioma, pituitary, and no-tumor. A pre-trained CNN backbone encodes the image stream, whereas a dedicated MLP encodes the radiomic stream. Both streams are fused via concatenation, gated, or bidirectional cross-modal attention strategies. Across nine experimental runs on a balanced 7,200 image dataset, all multimodal configurations outperform unimodal baselines with gated fusion achieving the best accuracy of 96.13%.
Indian Institute of Technology (IIT), Varanasi (BHU)（印度理工学院（贝拿勒斯印度教大学））；National Institute of Technology (NIT), Tiruchirappalli（国立理工学院蒂鲁奇拉帕利分校）
Brain tumour segmentation is an essential task in medical image processing. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumours for cancer diagnosis, from large number of MRI images, is both a difficult and time-consuming task. There is a need for automatic brain tumour image segmentation. The purpose of this project is to provide an automatic brain tumour segmentation method of MRI images to help locate the tumour accurately and quickly.
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e., peritumoral edema, necrotic core, enhancing and non-enhancing tumour core. Although brain tumours can easily be detected using multi-modal MRI, accurate tumor segmentation is a challenging task. Hence, using the data provided by the BraTS Challenge 2020, we propose a 3D volume-to-volume Generative Adversarial Network for segmentation of brain tumours. The model, called Vox2Vox, generates realistic segmentation outputs fro…
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images (mpMRI)is of great clinical importance, which defines tumour size, shape andappearance and provides abundant information for preoperative diag-nosis, treatment planning and survival prediction. Recent developmentson deep learning have significantly improved the performance of auto-mated medical image segmentation. In this paper, we compare severalstate-of-the-art convolutional neural network models for brain tumouri…
Imperial College London（帝国理工学院）；University of Oxford（牛津大学）
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven science, the success of machine learning, in particular supervised learning, largely depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire. It takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain tumour images that is able t…
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）
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagnosis is essential for successful treatment planning and magnetic resonance imaging is the principal imaging modality for diagnostic of brain tumours and their extent. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models on, a…
The Hospital for Sick Children（病童医院）；University of Toronto（多伦多大学）；Vector Institute（矢量研究所）
Deep learning techniques have greatly benefited computer-aided diagnostic systems. However, unlike other fields, in medical imaging, acquiring large fine-grained annotated datasets such as 3D tumour segmentation is challenging due to the high cost of manual annotation and privacy regulations. This has given interest to weakly-supervise methods to utilize the weakly labelled data for tumour segmentation. In this work, we propose a weakly supervised approach to obtain regions of interest using binary class labels. Furthermore, we propose a novel objective function to train the generator model ba…
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…
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）
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 …
Macquarie University（麦考瑞大学）；University of New South Wales（新南威尔士大学）
Accurate brain tumour segmentation is a crucial step towards improving disease diagnosis and proper treatment planning. In this paper, we propose a deep-learning based method to segment a brain tumour into its subregions: whole tumour, tumour core and enhancing tumour. The proposed architecture is a 3D convolutional neural network based on a variant of the U-Net architecture of Ronneberger et al. [17] with three main modifications: (i) a heavy encoder, light decoder structure using residual blocks (ii) employment of dense blocks instead of skip connections, and (iii) utilization of self-ensemb…
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…
Indian Institute of Technology Kharagpur（印度理工学院卡哈拉格普尔分校）
Automated brain tumour segmentation has the potential of making a massive improvement in disease diagnosis, surgery, monitoring and surveillance. However, this task is extremely challenging. Here, we describe our automated segmentation method using 2D CNNs that are based on U-Net. To deal with class imbalance effectively, we have used a weighted Dice loss function. We found that increasing the depth of the 'U' shape beyond a certain level results in a decrease in performance, so it is essential to choose an optimum depth. We also found that 3D contextual information cannot be captured by a sin…
The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non invasive diagnosis of paediatric brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classi…
Brain tumors are masses or abnormal growths of cells within the brain or the central spinal canal with symptoms such as headaches, seizures, weakness or numbness in the arms or legs, changes in personality or behaviour, nausea, vomiting, vision or hearing problems and dizziness. Conventional diagnosis of brain tumour involves some tests and procedure which may include the consideration of medical history, physical examination, imaging tests (such as CT or MRI scans), and biopsy (removal and examination of a small piece of the tumor tissue). These procedures, while effective, are mentally stren…
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
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segment brain tumors and to classify different categories of tumors from different MRI modalities. However, these networks are often black-box models and do not provide any evidence regarding the process they take to perform this task. Increasing transparency and interpretability of such deep learning techniques are necessary for the complete integrati…
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）
Expert interpretation of anatomical images of the human brain is the central part of neuro-radiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders representation of all possible anomalies very challenging. Hence, …
Romanian Institute of Science and Technology（罗马尼亚科学技术研究所）；Babeş-Bolyai University（巴贝什-博尧依大学）
Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods by learning the distribution of healthy images and identifying anomalies as outliers. In presence of an additional dataset of unlabelled data containing also anomalies, the task can be framed as a semi-supervised task with negative and unlabelled sample points. Recently, in Albu et al., 2020, we have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder, trained on unlabelled data. The di…
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）
Institute of Cancer Research（癌症研究所）；Universidad de Valladolid（巴利亚多利德大学）；AGH University of Science and Technology（AGH科技大学）；Cardiff University（卡迪夫大学）；Aberystwyth University（阿伯里斯特威斯大学）
Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology a…
Durham University（杜伦大学）；University of Exeter（埃克塞特大学）；County Durham and Darlington NHS Foundation Trust（达勒姆郡和达灵顿国民保健服务基金会信托）
Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to rec…
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…
McGill University（麦吉尔大学）；Centre for Intelligent Machines (CIM)（智能机器中心 (CIM)）；Department of Electrical and Computer Engineering（电气与计算机工程系）
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, accurate segmentation requires the inclusion of medical images (e.g., T1 post contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline advers…
Segmentation of head and neck (H\&N) tumours and prediction of patient outcome are crucial for patient's disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the lack of large multi-centre, multi-modal data with quality annotations. The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods of the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography images and prediction of progression-free survival in H\&N oropharynge…
Guilin University of Electronic Technology（桂林电子科技大学）；McGill University（麦吉尔大学）；Lady Davis Institute for Medical Research（戴维斯夫人医学研究所）
Radiomics has shown a capability for different types of cancers such as glioma to predict the clinical outcome. It can have a non-invasive means of evaluating the immunotherapy response prior to treatment. However, the use of deep convolutional neural networks (CNNs)-based radiomics requires large training image sets. To avoid this problem, we investigate a new imaging features that model distribution with a Gaussian mixture model (GMM) of learned 3D CNN features. Using these deep radiomic features (DRFs), we aim to predict the immune marker status (low versus high) and overall survival for gl…
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）
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research. Yet, a massive lack of annotations prevents the broad use of deep learning to analyze such data. So far, existing generative models fail to mitigate this problem because of frequent labeling errors. In this paper, we introduce a novel generative method which leverages real anatomical information to generate realistic image-label pairs of tumours. We construct a dual-pathway generator, for the anatomical image and label, trained in a cycle-consistent setup, constrained …
United Technology Research Center（联合技术研究中心）；Marche Polytechnic University（马尔凯理工大学）
Glioblastoma multiforme (GBM) is a fast-growing and highly invasive brain tumour, it tends to occur in adults between the ages of 45 and 70 and it accounts for 52 percent of all primary brain tumours. Usually, GBMs are detected by magnetic resonance images (MRI). Among MRI, Fluid-attenuated inversion recovery (FLAIR) sequence produces high quality digital tumour representation. Fast detection and segmentation techniques are needed for overcoming subjective medical doctors (MDs) judgment. In the present investigation, we intend to demonstrate by means of numerical experiments that topological f…
Massachusetts General Hospital（麻省总医院）；Massachusetts Institute of Technology（麻省理工学院）；National Taiwan University Hospital（台湾大学医院）；Vysioneer Inc.（Vysioneer公司）
Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This wo…
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）；Tencent（腾讯）；University of California, Los Angeles（加州大学洛杉矶分校）；Lenovo Research（联想研究院）
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segmentation methods. In this work, we present the first attempt to exploit the Transformer for multimodal brain tumor segmentation that is robust to any combinatorial subset of available modalities. Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incompl…
Chinese PLA General Hospital（中国人民解放军总医院）；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）；Beijing Jiaotong University（北京交通大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with no obvious structural lesions. To date, most deep-learning-based fMRI studies have employed functional connectivity (FC) as the basic feature for disease classification. However, FC is calculated on time series of predefined regions of interest and neglects detailed information contained in each voxel. Another drawback of using FC is the limited sample size for the training of deep models. The low representation ability of FC leads to poor performance in clinical practice, especially when dealing with m…
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 ($95\%$…
Shenzhen University（深圳大学）；Islamic Azad University（伊斯兰阿扎德大学）
The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions. The overlapping area among the intensity distribution of healthy, enhancing, non-enhancing, and edema regions makes the automatic segmentation a challenging task. Here, we show that a convolutional neural network trained on high-contrast images can transform the intensity distribution of brain lesions in its internal subregions. Specifically, a generative adversarial network (GAN) is extended to synthesize high-contrast images. A comparison of these synthetic images and real i…
South China University of Technology（华南理工大学）；University of Utah（犹他大学）
In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (U…
Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation
ShanghaiTech University（上海科技大学）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；Affiliated Hangzhou First People’s Hospital, Xihu University School of Medicine（西湖大学医学院附属杭州市第一人民医院）
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院与哈佛医学院戈登医学影像中心）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
A Novel Framework for Integrating 3D Ultrasound into Percutaneous Liver Tumour Ablation
Robarts Research Institute, Western University, London, Canada（罗巴茨研究所在西蒙大学伦敦分校）；School of Biomedical Engineering, Western University, London, Canada（生物医学工程学院，西蒙大学伦敦分校）；Department of Medical Biophysics, Western University, London, Canada（医学生物物理学系，西蒙大学伦敦分校）；Department of Electrical and Computer Engineering, Western University, London, Canada（电气与计算机工程系，西蒙大学伦敦分校）；Department of Medical Imaging, Western University, London, Canada（医学影像系，西蒙大学伦敦分校）
In recent years, non-invasive neuro-modulation methods such as Focused Ultrasound (FUS) have gained popularity. The aim of this work is to introduce the use of existing open-source technology for surgical navigation to the field of multimodal non-invasive brain stimulation. Unlike homegrown and commercial systems, the use of well-documented, well maintained, and freely available open-source components minimizes the learning curve, maximizes technology transfer outcome, and fosters reproducible science for complex, guided neuromodulation systems. The described system significantly lowers the en…
