Alzheimer's disease (AD) is a neuro-degenerative disease that can cause dementia and result severe reduction in brain function inhibiting simple tasks especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD induced dementia and unpaid care for people with AD related dementia is valued at $271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for early detection of AD. We then give an overview of our dataset that was from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and propose a deep Convolutional Neural Network (CNN) architecture consisting of 7,866,819 parameters. This model has three different convolutional branches with each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three class accuracy.
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Stanford University（斯坦福大学）；University of Missouri（密苏里大学）；Missouri Institute of Mental Health（密苏里精神卫生研究所）；Weill Cornell Medicine（威尔康奈尔医学院）；SRI International（SRI国际研究所）
WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs
Department of Radiology, Weill Cornell Medicine, New York, NY, USA（放射科，韦尔·科恩医学中心，纽约，纽约州，美国）；Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA（精神病学与行为科学系，斯坦福大学，斯坦福，加利福尼亚州，美国）；Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA（电气与计算机工程系，康奈尔大学，伊萨卡，纽约州，美国）
School of Information Technology（信息技术学院）；University of Cincinnati（辛辛那提大学）
Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset o…
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network performs so well for image analysis task. How CNN analyzes an image and discriminates among samples of different classes are usually considered as non-transparent. As a result, it becomes difficult to apply CNN based approaches in clinical procedures and automated disease diagnosis systems. In this paper, we consider this issue and work on visualizing and understanding the decision…
Evaluation of 3D Counterfactual Brain MRI Generation
Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA Department of Radiology, Stanford University, Stanford, CA, USA Dept. of Psychiatry \& Behavioral Sciences, Stanford University, Stanford, CA, USA Department of Computer Science, Stanford University, Stanford, CA, USA Department of Bioengineering, Stanford University, Stanford, CA, USA Department of Electrical Engineering, Stanford University, Stanford, CA, USA
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, …
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Chung-Ang University（中央大学）
We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network. Our embedding network learns features that can resolve detailed structures in the brain without the need for high-resolution training images, which are often unavailable and expensive to acquire. Alternatively, the encoder-decoder learns global structures by means of striding and max pooling. Our embedding network complements the encode…
University of Oulu（奥卢大学）；KFUPM（法赫德国王石油与矿业大学）；King Fahd Specialist Hospital Dammam（达曼法赫德国王专科医院）
Introduction: Multiple Sclerosis (MS) is a chronic disease that affects millions of people across the globe. MS can critically affect different organs of the central nervous system such as the eyes, the spinal cord, and the brain. Background: To help physicians in diagnosing MS lesions, computer-aided methods are widely used. In this regard, a considerable research has been carried out in the area of automatic detection and segmentation of MS lesions in magnetic resonance images (MRIs). Methodology: In this study, we review the different approaches that have been used in computer-aided detecti…
Institute of Medical Technology and Intelligent Systems, Hamburg University of Technology（汉堡工业大学医疗技术与智能系统研究所）；jung diagnostics GmbH（jung diagnostics GmbH公司）；Hamburg University of Technology（汉堡工业大学）
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consuming process. Recently, unsupervised anomaly detection (UAD) in brain MRI with deep learning has shown promising results to provide a quick, initial assessment. So far, these methods only rely on the visual appearance of healthy brain anatomy for anomaly detection. Another biomarker for abnormal brain development is the deviation between the brain age and the chronological age, which is unexplored in combination with …
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation
Vo Thien Nhan Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Vietnam（越南胡志明市技术大学（HUTECH）工程学院）；Ho Bac Nam Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM), Vietnam（越南胡志明市技术大学（HCMUT）计算机科学与工程学院，越南胡志明市国家大学（VNU-HCM））
Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated images for each pathology, a strenuous task. To tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a Variational Autoencoder (VAE). Previous work on UAD adopted a 2D approach, meaning that MRIs are processed as a collection of independent slices. Yet, it does not fully exploit the spatial information contained in …
Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion
Dept. of Math and Computer Science, University of Catania（数学与计算机科学系，卡塔尼亚大学）；Centre for Medical Image Computing, University College London（医学影像计算中心，伦敦大学学院）；MIFT Department, University of Messina（MIFT系，梅斯ina大学）
提出 BrLP 时空模型，通过在小型潜在空间运行、整合受试者元数据与疾病动力学先验，并引入 LAS 算法，实现了 3D 脑部 MRI 个体化疾病进展的高精度预测。
College of Information and Computer Sciences（信息与计算机科学学院）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover that when we split that data into training and testing sets at the subject level, we are not able to obtain similar performance, bringing the validity of many of the previous studies into question. Furthermore, we point out that previous works use different subsets of the ADNI data, making comparison across similar works tricky. In this study, we …
Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation
Sorbonne Université（索邦大学）；Institut du Cerveau – Paris Brain Institute - ICM（大脑研究所——巴黎脑研究所 - ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié-Salpêtrière（皮蒂-萨尔皮特里医院）
Faculty of Science, Northern Border University（北部边境大学理学院）；SEU Saudi Electronic University（沙特电子大学）
Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from …
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Central South University（中南大学）
Alzheimer's disease is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior guided adversarial representation learning and hypergraph perceptual network (PGARL-HPN) is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from the anatomical knowledge is estimated…
Indian Institute of Science（印度科学院）；College of Engineering Trivandrum（特里凡得琅工程学院）
The revolutionary developments in the field of supervised machine learning have paved way to the development of CAD tools for assisting doctors in diagnosis. Recently, the former has been employed in the prediction of neurological disorders such as Alzheimer's disease. We propose a CAD (Computer Aided Diagnosis tool for differentiating neural lesions caused by CVA (Cerebrovascular Accident) from the lesions caused by other neural disorders by using Non-negative Matrix Factorisation (NMF) and Haralick features for feature extraction and SVM (Support Vector Machine) for pattern recognition. We a…
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
University of Engineering and Technology Taxila（塔克西拉工程技术大学）；University of Central Florida（中佛罗里达大学）
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. An early detection can prevent the patient from further damage of the brain cells and hence avoid permanent memory loss. In past few years, various automatic tools and techniques have been proposed for diagnosis of AD. Several methods focus on fast, accurate and early detection of the disease to minimize the loss to patients mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for A…
MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder
Department of Radiation Oncology, TUM University Hospital, Munich, Germany（辐射肿瘤科，技术大学慕尼黑医院，慕尼黑，德国）；Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM)（医疗与医学人工智能教研室，技术大学慕尼黑（TUM））；TUM University Hospital, Munich, Germany（技术大学慕尼黑医院，慕尼黑，德国）；Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM)（图像引导诊断与治疗人工智能教研室，技术大学慕尼黑（TUM））；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）；Department of Computing, Imperial College London, London, UK（计算系，伦敦帝国学院，伦敦，英国）；Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑合作伙伴站点，慕尼黑，德国）；Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany（辐射医学研究所（IRM），辐射科学部门（DRS），慕尼黑海德堡中心，慕尼黑，德国）
Alzheimer's disease (AD) is one of the most common public health issues the world is facing today. This disease has a high prevalence primarily in the elderly accompanying memory loss and cognitive decline. AD detection is a challenging task which many authors have developed numerous computerized automatic diagnosis systems utilizing neuroimaging and other clinical data. MRI scans provide high-intensity visible features, making these scans the most widely used brain imaging technique. In recent years deep learning has achieved leading success in medical image analysis. But a relatively little …
Comparison of different segmentation algorithms on brain volume and fractal dimension in infant brain MRIs
Laboratory for Motion Analysis, Devision of Paediatric Orthopaedic, Children's Hospital of Eastern Switzerland（东部瑞士儿童医院运动分析实验室）；Lucerne University of Applied Sciences and Arts（卢塞恩应用科学与艺术大学）；TOELT llc, Machine Learning Research and Development（TOELT公司，机器学习研究与开发）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学，计算机与信息科学学院）
Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；University of California San Diego（加州大学圣地亚哥分校）；SRI International（斯坦福国际研究院）
Many neurological diseases are characterized by gradual deterioration of brain structure and function. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits…
School of Biomedical Engineering & Imaging Sciences, King’s College London, UK（伦敦国王学院生物医学工程与成像科学学院）；Department of Radiology, University Hospitals Leuven, Belgium（鲁汶大学医院放射科）；Institute for Women’s Health, University College London, UK（伦敦大学学院妇女健康研究所）；Department of Obstetrics and Gynaecology, University Hospitals Leuven, Belgium（鲁汶大学医院妇产科）；Department of Gynecology and Obstetrics, University Hospitals Tanta, Egypt（坦塔大学医院妇产科）；Department of Biomedical Imaging and Image-guided Therapy Medical University of Vienna, Austria（维也纳医科大学生物医学成像与图像引导治疗系）
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and robustness. In fetal brain MRI, abnormalities exacerbate the variability of the developing brain anatomy compared to non-pathological cases. A small number of abnormal cases, as is typically available in clinical datasets used for training, are unlikely to fairly represent the rich variability of abnormal developing brains. This leads machine learning systems trained by maximizing the average performance to be biased t…
Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. One popular choice for explaining deep image models is occluding regions of the image to isolate their influence on the prediction. However, existing methods for occluding patches of brain scans generate images outside the distribution to whi…
GenFAR: A generalized representation of brain structure, derived from 49,246 multi-cohort MRIs via deep learning
University of Pennsylvania（宾夕法尼亚大学）；University of Melbourne（墨尔本大学）；University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院）；CSIRO Health and Biosecurity（联邦科学与工业研究组织健康与生物安全部）；CSIRO（联邦科学与工业研究组织）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Washington（华盛顿大学）；Wake Forest School of Medicine（维克森林医学院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Indiana University（印第安纳大学）；University of Southern California（南加利福尼亚大学）
Istituto Nazionale di Fisica Nucleare（意大利国家核物理研究所）；Università degli Studi di Bari（巴里大学）；INAF - Astronomical Observatory of Capodimonte（意大利国家天体物理研究所-卡波迪蒙特天文台）；Università degli Studi di Genova（热那亚大学）；Università degli Studi di Napoli Federico II（那不勒斯费德里克二世大学）；Istituto di Studi sui Sistemi Intelligenti per l’Automazione, CNR（意大利国家研究委员会智能系统与自动化研究所）
Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we co…
University College London（伦敦大学学院）；Biomediq A/S（Biomediq公司）；University of Copenhagen（哥本哈根大学）
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--. However, most of the existing retrospective datasets do not consist of FLAIR sequences. Existing missing modality imputation methods separate the process of imputation, and the process of segmentation. In this paper, we propose a method to link both modality imputation and segmentation using…
University of Ottawa（渥太华大学）；Children’s Hospital of Eastern Ontario（东安大略儿童医院）
Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) with PMG and controls from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG MRIs and control MRIs are subtle and the true distribution of the features of the disease is unknown. This mak…
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）
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…
MRExtrap: Longitudinal Aging of Brain MRIs using Linear Modeling in Latent Space
University of Tübingen（图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Cluster of Excellence: Machine Learning – New Perspectives for Science（卓越中心：机器学习——科学的新视角）；University of Lucerne（卢塞恩大学）
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…
Rowan University（罗文大学）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Magnetic resonance imaging (MRI) is routinely used for brain tumor diagnosis, treatment planning, and post-treatment surveillance. Recently, various models based on deep neural networks have been proposed for the pixel-level segmentation of tumors in brain MRIs. However, the structural variations, spatial dissimilarities, and intensity inhomogeneity in MRIs make segmentation a challenging task. We propose a new end-to-end brain tumor segmentation architecture based on U-Net that integrates Inception modules and dilated convolutions into its contracting and expanding paths. This allows us to ex…
Lehigh University（里海大学）；University of Pennsylvania（宾夕法尼亚大学）
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting…
Alzheimer's disease (AD) is the most prevalent form of dementia. Traditional methods cannot achieve efficient and accurate diagnosis of AD. In this paper, we introduce a novel method based on dynamic functional connectivity (dFC) that can effectively capture changes in the brain. We compare and combine four different types of features including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dFC and the adjacency matrix of different brain structures between subjects. We use graph convolution network (GCN) which consider the similarity of brain structure between pati…
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied in neuroimaging can not efficiently fuse the functional and structural information from multi-modal neuroimages. In this work, a novel cross-modal transformer generative adversarial network(CT-GAN) is proposed to fuse functional information contained in resting-state functional magnetic resonance imaging (rs-fMRI) and structural information contained in Diffusion Tensor Imaging (DTI). The developed bi-attention mech…
