Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great interest to the medical image analysis community as it i) relieves from the need of vast amounts of manually segmented training data---a necessity for and pitfall of current supervised Deep Learning---and ii) theoretically allows to detect arbitrary, even rare pathologies which supervised approaches might fail to find. To date, the experimental design of most works hinders a valid comparison, because i) they are evaluated against different datasets and different pathologies, ii) use different image resolutions and iii) different model architectures with varying complexity. The intent of this work is to establish comparability among recent methods by utilizing a single architecture, a single resolution and the same dataset(s). Besides providing a ranking of the methods, we also try to answer questions like i) how many healthy training subjects are needed to model normality and ii) if the reviewed approaches are also sensitive to domain shift. Further, we identify open challenges and provide suggestions for future community efforts and research directions.
Brain tumor segmentation is a challenging problem in medical image analysis. The endpoint is to generate the salient masks that accurately identify brain tumor regions in an fMRI screening. In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated network to highlight and segment the salient regions from fMRI images. This feature enables us to eliminate the necessity of having to explicitly point towards the damaged area(external tissue localization) and classify(classification) as per classical c…
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…
TU Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；ETH Zurich（苏黎世联邦理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI have shown promising performance, but face difficulties in modeling distributions with high fidelity, which is crucial for accurate delineation of particularly small lesions. Here, similar to these previous works, we model the distribution of healthy brain MRI to localize pathologies from erroneous reconstructions. However, to achieve improved reconstruction fidelity at hi…
University of Iceland（冰岛大学）；Landspitali - University Hospital（兰斯皮塔利大学医院）；The Icelandic Heart Association（冰岛心脏协会）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Lesions that appear hyperintense in both Fluid Attenuated Inversion Recovery (FLAIR) and T2-weighted magnetic resonance images (MRIs) of the human brain are common in the brains of the elderly population and may be caused by ischemia or demyelination. Lesions are biomarkers for various neurodegenerative diseases, making accurate quantification of them important for both disease diagnosis and progression. Automatic lesion detection using supervised learning requires manually annotated images, which can often be impractical to acquire. Unsupervised lesion detection, on the other hand, does not r…
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
University of Girona（赫罗纳大学）；University of Burgundy（勃艮第大学）；University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
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, …
National Institute of Neurological Disorders and Stroke（美国国家神经疾病与中风研究所）；University of Houston（休斯顿大学）
Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoenco…
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this paper, we propose a predicti…
Comparative Evaluation of Transfer Learning for Classification of Brain Tumor Using MRI
Florida Institute of Technology（佛罗里达理工学院）；Daffodil International University（达福德国际大学）；University of North Texas（北德克萨斯大学）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Indian Institute of Science Education and Research, Kolkata（印度科学教育与研究所，加尔各答）；Institute of Engineering & Management, Kolkata（工程与管理学院，加尔各答）；Indian Institute of Technology, Kanpur（印度理工学院，坎普尔）
In this paper, a comparative study between proposed hyper kurtosis based modified duo-histogram equalization (HKMDHE) algorithm and contrast limited adaptive histogram enhancement (CLAHE) has been presented for the implementation of contrast enhancement and brightness preservation of low contrast human brain CT scan images. In HKMDHE algorithm, contrast enhancement is done on the hyper-kurtosis based application. The results are very promising of proposed HKMDHE technique with improved PSNR values and lesser AMMBE values than CLAHE technique.
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sparse and complex clinical data. Data science approaches typically address this challenge by performing feature learning in order to build more reliable and informative feature representations from clinical data followed by supervised learning. In this paper, we propose a predicti…
Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳大数据研究院）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机学院）；Pazhou Lab（琶洲实验室）
Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding. However, existing methods seldom study the various importance of reconstructed patches and the symmetry of anatomical structures, when they are applied to 3D medical images. In this paper we propose a novel Attentive Symmetric Auto-encoder (ASA) based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. We conjecture that forcing the auto-encoder to recove…
Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification
Monash University Malaysia（莫纳什大学马来西亚校区）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
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）
Samsung Medical Center（三星医疗中心）；Sungkyunkwan University School of Medicine（成均馆大学医学院）；Sungkyunkwan University（成均馆大学）；Columbia University（哥伦比亚大学）；University of Maryland（马里兰大学）
Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (n=105) and patients with PD (n=105), multiple systemic atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard…
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 …
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study
University of Dhaka（达卡大学）；Utah State University（犹他州立大学）；Noakhali Science and Technology University（诺阿卡利科学技术大学）；Daffodil International University（水仙花国际大学）
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；Helmholtz AI, Helmholtz Munich（亥姆霍兹慕尼黑人工智能中心）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；School of Biomedical Engineering and Imaging Sciences, King's College London（伦敦国王学院生物医学工程与影像科学学院）
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
Univ. Rennes（雷恩大学）；INSA Rennes（雷恩国立应用科学学院）；CNRS（法国国家科学研究中心）；University Hospital Bonn（波恩大学医院）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；Technical University of Munich（慕尼黑工业大学）
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload. Segmentation is a fundamental limiting precursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during training, where convolutional neural network (CNN) based autoencoders (AEs) and variational autoencoders (VAEs) are considered a de facto approach for recon…
ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Stanford University（斯坦福大学）；Stanford Stroke Center（斯坦福中风中心）；Institute for Biomedical Engineering（生物医学工程研究所）
We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. Based on professional neuroradiologists' evaluations and diverse metrics with respect to quality and diversity of the generated synthetic brain images, we present two networks, the Introspective Variational Autoencoder and the Style-Based GAN, that qualify for data augmentation in the medical field, where information is saved in a dispatched and inhomogeneous way and access to it is in many aspects restricted.
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Autoencoders (VAEs), opened up new avenues for probabilistic modeling. Modelling of volumetric data has remained a challenge, however, because constraints on available computation and training data make it difficult effectively leverage VAEs, which are well-developed for 2D images. We propose a method to model 3D MR brain volumes distribution by combi…
Beijing-Dublin International College（北京-都柏林国际学院）；University College Dublin（都柏林大学）
This technical report presents a comparative analysis of existing deep learning (DL) based approaches for brain tumor segmentation with missing MRI modalities. Approaches evaluated include the Adversarial Co-training Network (ACN) and a combination of mmGAN and DeepMedic. A more stable and easy-to-use version of mmGAN is also open-sourced at a GitHub repository. Using the BraTS2018 dataset, this work demonstrates that the state-of-the-art ACN performs better especially when T1c is missing. While a simple combination of mmGAN and DeepMedic also shows strong potentials when only one MRI modality…
Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
Department of Computer Science, Quaid-e-Awam University of Engineering, Sciences & Technology（夸迪-艾瓦姆工程、科学与技术大学计算机科学系）；Department of Artificial Intelligence, Quaid-e-Awam University of Engineering, Sciences & Technology（夸迪-艾瓦姆工程、科学与技术大学人工智能系）；The Faculty of Artificial Intelligence and Cyber Security, Universiti Teknikal Malaysia Melaka（马来西亚梅拉卡技术大学人工智能与网络安全学院）；Department of Data Science, Quaid-e-Awam University of Engineering, Sciences & Technology（夸迪-艾瓦姆工程、科学与技术大学数据科学系）；Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London（东伦敦大学建筑、计算与工程学院计算机科学与数字技术系）
University of Waterloo（滑铁卢大学）；University of Ontario Institute of Technology（安大略理工大学）
Autoencoders have been recently used for encoding medical images. In this study, we design and validate a new framework for retrieving medical images by classifying Radon projections, compressed in the deepest layer of an autoencoder. As the autoencoder reduces the dimensionality, a multilayer perceptron (MLP) can be employed to classify the images. The integration of MLP promotes a rather shallow learning architecture which makes the training faster. We conducted a comparative study to examine the capabilities of autoencoders for different inputs such as raw images, Histogram of Oriented Grad…
Autoencoders have been widely used for dimensional reduction and feature extraction. Various types of autoencoders have been proposed by introducing regularization terms. Most of these regularizations improve representation learning by constraining the weights in the encoder part, which maps input into hidden nodes and affects the generation of features. In this study, we show that a constraint to the decoder can also significantly improve its performance because the decoder determines how the latent variables contribute to the reconstruction of input. Inspired by the structural modal analysis…
Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging
Department of Electrical and Information Engineering (DIEI), University of Cassino and Southern Lazio（电气与信息工程系（DIEI），卡斯诺和南部拉齐亚大学）；Diagnostic Image Analysis Group, Radboud University Medical Center（影像诊断分析组，拉德堡德大学医学中心）；Department of Medical Imaging, Radboud University Medical Center（医学成像系，拉德堡德大学医学中心）
RIKEN Center for Brain Science（理化学研究所脑科学中心）；Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）；Univ Montpellier（蒙彼利埃大学）；IMT Mines Alès（法国国立高等矿业电信学校阿莱斯分校）
We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registration of images to a standard space. Automation of this pipeline is necessary to analyze the large volume of data in the genome-wide whole-brain dataset, and to process images that have varying intensity profiles and expression patterns with minimal human bias. To reduce the number of labelled images required for training, we develop a semi-supervi…
TU Munich（慕尼黑工业大学）；Klinikum Rechts der Isar（Rechts der Isar 医院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such unsupervised anomaly detection approaches has been made in the medical domain, based on statistical methods, content-based retrieval, clustering and recently also deep learning. Previous approaches towards deep unsupervised anomaly detection model patches of normal anatomy with variants of Autoencoders or GANs, and detect anomalies either as outliers in the learned feature space or from large reconstruction errors. I…
Kavli Institute for Systems Neuroscience, NTNU（挪威科技大学卡弗里系统神经科学研究所）
Early diagnosis and accurate segmentation of brain tumors are imperative for successful treatment. Unfortunately, manual segmentation is time consuming, costly and despite extensive human expertise often inaccurate. Here, we present an MRI-based tumor segmentation framework using an autoencoder-regularized 3D-convolutional neural network. We trained the model on manually segmented structural T1, T1ce, T2, and Flair MRI images of 335 patients with tumors of variable severity, size and location. We then tested the model using independent data of 125 patients and successfully segmented brain tumo…
Diffusion MRI (dMRI) of the developing brain can provide valuable insights into the white matter development. However, slice thickness in fetal dMRI is typically high (i.e., 3-5 mm) to freeze the in-plane motion, which reduces the sensitivity of the dMRI signal to the underlying anatomy. In this study, we aim at overcoming this problem by using autoencoders to learn unsupervised efficient representations of brain slices in a latent space, using raw dMRI signals and their spherical harmonics (SH) representation. We first learn and quantitatively validate the autoencoders on the developing Human…
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Emory University School of Medicine（埃默里大学医学院）；University of Florida School of Medicine（佛罗里达大学医学院）；Brown University（布朗大学）
Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning a…
Vanderbilt University（范德堡大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）；Vanderbilt University Medical Center（范德堡大学医学中心）
Epilepsy is the fourth most common neurological disorder and affects people of all ages worldwide. Deep Brain Stimulation (DBS) has emerged as an alternative treatment option when anti-epileptic drugs or resective surgery cannot lead to satisfactory outcomes. To facilitate the planning of the procedure and for its standardization, it is desirable to develop an algorithm to automatically localize the DBS stimulation target, i.e., Anterior Nucleus of Thalamus (ANT), which is a challenging target to plan. In this work, we perform an extensive comparative study by benchmarking various localization…
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains a formidable challenge to learn brain region information and discover disease mechanisms from various magnetic resonance images (MRI). In this paper, we propose a simple but highly efficient end-to-end model, a multiscale autoencoder with structural-functional attention network (MASAN) to extract disease-related representations using T1-weighted Imaging (T1WI) and functional MRI (fMRI). Based on the attention mechan…
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
Kingston University（金史密斯大学）；Quantum AI (QAI) Research Group（量子人工智能研究组）；School of Computer Science and Mathematics (CSM)（计算机科学与数学学院）；Faculty of Engineering, Computing and the Environment (ECE)（工程、计算与环境学院）
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai Mental Health Center（上海市精神卫生中心）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）
Subjective cognitive decline (SCD) is a preclinical stage of Alzheimer's disease (AD) which occurs even before mild cognitive impairment (MCI). Progressive SCD will convert to MCI with the potential of further evolving to AD. Therefore, early identification of progressive SCD with neuroimaging techniques (e.g., structural MRI) is of great clinical value for early intervention of AD. However, existing MRI-based machine/deep learning methods usually suffer the small-sample-size problem which poses a great challenge to related neuroimaging analysis. The central question we aim to tackle in this p…
Université de Sherbrooke（舍布鲁克大学）；Univ. Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；CEA（法国原子能和替代能源委员会）
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tr…
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches
AGH University of Krakow（克拉科夫AGH大学）；Universitas Pembangunan Nasional Veteran Yogyakarta（日惹国家发展老兵大学）；Hohai University（河海大学）；Universitas Ahmad Dahlan（艾哈迈德·达赫兰大学）
Segmentation of brain structures in a large dataset of magnetic resonance images (MRI) necessitates automatic segmentation instead of manual tracing. Automatic segmentation methods provide a much-needed alternative to manual segmentation which is both labor intensive and time-consuming. Among brain structures, the hippocampus presents a challenging segmentation task due to its irregular shape, small size, and unclear edges. In this work, we use T1-weighted MRI of 426 subjects to validate the approach and compare three automatic segmentation methods: FreeSurfer, LocalInfo, and ABSS. Four evalua…
Ohio University（俄亥俄大学）；Huazhong Normal University（华中师范大学）；University of Kentucky（肯塔基大学）
In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully convolutional network (FCN) method and three state-of-the-art super-resolution solutions, ESPCN [26], SRGAN [17] and PRSR [7]. The FCN solution, U-Convert-Net, carries out mapping of 1.5T-to-3T slices through a U-Net-like architecture, with 3D neighborhood information integrated through a multi-view ensemble. The pros and cons of the models, as well the associated evaluation metrics, are measured with experiments an…
