RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation
University of Eastern Finland（东芬兰大学）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）；Sapienza Università di Roma（罗马第一大学）
Juan Miguel Valverde, Artem Shatillo, Riccardo de Feo, Olli Gröhn, Alejandra Sierra, Jussi Tohka
We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2.
AI Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学AI·维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）；Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi（费米中心——恩里科·费米物理历史博物馆与研究中心）
Manual segmentation of rodent brain lesions from magnetic resonance images (MRIs) is an arduous, time-consuming and subjective task that is highly important in pre-clinical research. Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation. The existing tools for performing automatic lesion segmentation in rodents are constrained by strict assumptions about the data. Deep learning has been successfully used for medical image segmentation. However, there has not been any deep learning approach…
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…
Faculty of Information Technology, University of Science, VNU-HCM（越南胡志明市国家大学科学大学信息技术学院）；SBEI School №1228 ”Lefortovo”（第1228号“列福尔托沃”基础普通教育学校）
In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especiall…
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…
Lesion detection in brain Magnetic Resonance Images (MRI) remains a challenging task. State-of-the-art approaches are mostly based on supervised learning making use of large annotated datasets. Human beings, on the other hand, even non-experts, can detect most abnormal lesions after seeing a handful of healthy brain images. Replicating this capability of using prior information on the appearance of healthy brain structure to detect lesions can help computers achieve human level abnormality detection, specifically reducing the need for numerous labeled examples and bettering generalization of p…
P-Count: Persistence-based Counting of White Matter Hyperintensities in Brain MRI
Martinos Center for Biomedical Imaging, MGH and Harvard Medical School（马蒂诺斯生物医学成像中心，麻省总医院与哈佛医学院）；Center for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital（哥本哈根大学医院丹麦磁共振研究中心）；Computer Science and Artificial Intelligence Laboratory, MIT（麻省理工学院计算机科学与人工智能实验室）
A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland（东芬兰大学A.I.维尔塔宁分子科学研究所）；Charles River Discovery Services（查尔斯河发现服务公司）
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory r…
Deep neural networks have become the gold-standard approach for the automated segmentation of 3D medical images. Their full acceptance by clinicians remains however hampered by the lack of intelligible uncertainty assessment of the provided results. Most approaches to quantify their uncertainty, such as the popular Monte Carlo dropout, restrict to some measure of uncertainty in prediction at the voxel level. In addition not to be clearly related to genuine medical uncertainty, this is not clinically satisfying as most objects of interest (e.g. brain lesions) are made of groups of voxels whose …
Stanford University（斯坦福大学）；Oslo University Hospital（奥斯陆大学医院）
Deep learning has proven to be an essential tool for medical image analysis. However, the need for accurately labeled input data, often requiring time- and labor-intensive annotation by experts, is a major limitation to the use of deep learning. One solution to this challenge is to allow for use of coarse or noisy labels, which could permit more efficient and scalable labeling of images. In this work, we develop a lopsided loss function based on entropy regularization that assumes the existence of a nontrivial false negative rate in the target annotations. Starting with a carefully annotated b…
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…
Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification
University of Exeter（埃克塞特大学）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Sorbonne Université（索邦大学）
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Emory University（埃默里大学）；Atlanta VA Medical Center（亚特兰大退伍军人事务医疗中心）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University - Newark（罗格斯大学纽瓦克分校）
We propose a fully 3D multi-path convolutional network to predict stroke lesions from 3D brain MRI images. Our multi-path model has independent encoders for different modalities containing residual convolutional blocks, weighted multi-path feature fusion from different modalities, and weighted fusion modules to combine encoder and decoder features. Compared to existing 3D CNNs like DeepMedic, 3D U-Net, and AnatomyNet, our networks achieves the highest statistically significant cross-validation accuracy of 60.5% on the large ATLAS benchmark of 220 patients. We also test our model on multi-modal…
icometrix（icometrix公司）；Technical University of Munich（慕尼黑工业大学）
Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TBI). It allows identification of most lesion types and determines the need of surgical or alternative therapeutic procedures. However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict TBI lesions by using CT-derived radiomics descriptors. Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial…
Henry M. Jackson Foundation（亨利·M·杰克逊基金会）；National Institute of Health（美国国立卫生研究院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Multiple Sclerosis (MS) is an autoimmune disease that leads to lesions in the central nervous system. Magnetic resonance (MR) images provide sufficient imaging contrast to visualize and detect lesions, particularly those in the white matter. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Therefore robust and accurate segmentation of white matter lesions from MR images can provide important information about the disease status and progression. In this paper, we propose a fully convolutional neural network (CNN…
Universidad Técnica Particular de Loja（洛哈技术大学）；University of Waterloo（滑铁卢大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；Consejo Superior de Investigaciones Científicas (CSIC)（西班牙国家研究委员会）
Medical brain image analysis is a necessary step in Computer Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 do…
New Jersey Institute of Technology（新泽西理工学院）；Kessler Foundation（凯斯勒基金会）；Rutgers – New Jersey Medical School（罗格斯大学新泽西医学院）；Medical College of Wisconsin（威斯康星医学院）；Rutgers University – Newark（罗格斯大学纽瓦克分校）
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. We propose a multi-modal multi-path convolutional neural network system for automating stroke lesion segmentation. Our system has nine end-to-end UNets that take as input 2-dimensional (2D) slices and examines all three planes with three different normalizations. Outputs from these nine total paths are concatenated into a 3D volume that is then passed to a 3D convolutional neural network to output a final lesion mask. We tr…
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…
State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters
Center for Data Science, Nell Hodgson Woodruff School of Nursing（内尔·霍奇森·伍德拉夫护理学院数据科学中心）；Emory University（埃默里大学）；University of South Carolina（南卡罗来纳大学）；Georgia State University（佐治亚州立大学）
Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this …
Philips Healthcare（飞利浦医疗）；University of Washington（华盛顿大学）
Patient motion during the magnetic resonance imaging (MRI) acquisition process results in motion artifacts, which limits the ability of radiologists to provide a quantitative assessment of a condition visualized. Often times, radiologists either "see through" the artifacts with reduced diagnostic confidence, or the MR scans are rejected and patients are asked to be recalled and re-scanned. Presently, there are many published approaches that focus on MRI artifact detection and correction. However, the key question of the bias exhibited by these algorithms on motion corrupted MRI images is still…
Pathological brain appearances may be so heterogeneous as to be intelligible only as anomalies, defined by their deviation from normality rather than any specific pathological characteristic. Amongst the hardest tasks in medical imaging, detecting such anomalies requires models of the normal brain that combine compactness with the expressivity of the complex, long-range interactions that characterise its structural organisation. These are requirements transformers have arguably greater potential to satisfy than other current candidate architectures, but their application has been inhibited by …
USC Stevens Neuroimaging and Informatics Institute（南加州大学史蒂文斯神经影像与信息学研究所）；Zilkha Neurogenetic Institute, Keck School of Medicine of USC（南加州大学凯克医学院齐尔卡神经遗传学研究所）；Martinos Center for Biomedical Imaging, Massachusetts General Hospital（麻省总医院马蒂诺斯生物医学成像中心）；Yale University（耶鲁大学）；University of Iowa（爱荷华大学）；Augusta University（奥古斯塔大学）；University of Texas（德克萨斯大学）；Kennedy Krieger Institute, Johns Hopkins University（约翰斯·霍普金斯大学肯尼迪·克里格研究所）
While ischemic stroke is a leading cause of death worldwide, there has been little success translating putative cerebroprotectants from rodent preclinical trials to human patients. We investigated computational image-based assessment tools for practical improvement of the quality, scalability, and outlook for large scale preclinical screening for potential therapeutic interventions in rodent models. We developed, evaluated, and deployed a pipeline for image-based stroke outcome quantification for the Stroke Preclinical Assessment Network (SPAN), a multi-site, multi-arm, multi-stage study evalu…
The rapid increment of morbidity of brain stroke in the last few years have been a driving force towards fast and accurate segmentation of stroke lesions from brain MRI images. With the recent development of deep-learning, computer-aided and segmentation methods of ischemic stroke lesions have been useful for clinicians in early diagnosis and treatment planning. However, most of these methods suffer from inaccurate and unreliable segmentation results because of their inability to capture sufficient contextual features from the MRI volumes. To meet these requirements, 3D convolutional neural ne…
Memorial Sloan Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Purpose: Lesion segmentation in medical imaging is key to evaluating treatment response. We have recently shown that reinforcement learning can be applied to radiological images for lesion localization. Furthermore, we demonstrated that reinforcement learning addresses important limitations of supervised deep learning; namely, it can eliminate the requirement for large amounts of annotated training data and can provide valuable intuition lacking in supervised approaches. However, we did not address the fundamental task of lesion/structure-of-interest segmentation. Here we introduce a method co…
Modality-Agnostic Input Channels Enable Segmentation of Brain lesions in Multimodal MRI with Sequences Unavailable During Training
Department of Engineering Science, University of Oxford, Oxford, UK（工程科学系，牛津大学，牛津，英国）；Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK（牛津大学临床神经科学系，牛津，英国）
Oslo Metropolitan University（奥斯陆城市大学）；Norwegian University of Science and Technology（挪威科技大学）；Kavli Institute for Systems Neuroscience（卡夫利系统神经科学研究所）
Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle human data. Nonetheless, these tools are not equally useful to handle the data derived from animal studies, especially from rodents. This represents a major problem to the field because rodent studies generate larger datasets from larger populations, which implies that preprocessing these images manually to remove the skull becomes a bottleneck in the data analysis pipeline.…
PGR-Net: Prior-Guided ROI Reasoning Network for Brain Tumor MRI Segmentation
College of Information Engineering, Capital Normal University（首都师范大学信息工程学院）；School of Artificial Intelligence, China University of Mining and Technology-Beijing（中国矿业大学(北京)人工智能学院）
Henry Jackson Foundation（亨利·杰克逊基金会）；National Institute of Health（美国国立卫生研究院）
Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a …
Univ Lyon（里昂大学）；INSA‐Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安让·莫奈大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS UMR 5220（CREATIS联合研究单位5220）；U1206（U1206研究单位）
Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in training a voxel-level binary classifier on a set of feature vectors extracted from normal and pathological areas in patients' scans. However, many pathologies (such as epilepsy) are characterized by lesions that may be located anywhere in the brain, have various shapes, sizes and texture. An adequate representation of such a heterogeneity requires a significant amount of annotated data which is a major issue in the…
A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；Beijing Institute of Technology（北京理工大学）；Capital Medical University（首都医科大学）；Beijing Tiantan Hospital（北京天坛医院）
Beijing Institute of Technology（北京理工大学）；Peking University Third Hospital（北京大学第三医院）；Deepwise AI Lab（深度智耀人工智能实验室）
Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other "mix"-based methods, such as Mixup and CutMix, CarveMix stochastically combines two existing labeled images to generate new…
Monitoring the progression of lesions is important for clinical response. Summary statistics such as lesion volume are objective and easy to interpret, which can help clinicians assess lesion growth or decay. CNNs are commonly used in medical image segmentation for their ability to produce useful features within large contexts and their associated efficient iterative patch-based training. Many CNN architectures require hundreds of thousands parameters to yield a good segmentation. In this work, an efficient, computationally inexpensive CNN is implemented to estimate the number of lesion voxels…
All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）；Deutsches Konsortium für Translationale Krebsforschung (DKTK)（德国转化癌症研究联盟（DKTK））；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）
Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the proposed method can be used as a data augmentation module to generate synthetic images for training brain image segmentation networks. Experiments on mult…
King’s College London（伦敦国王学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民健康服务基金会信托）；NVIDIA（英伟达）；Dementia Research Centre, UCL Institute of Neurology, UCL（伦敦大学学院痴呆研究中心、伦敦大学学院神经学研究所、伦敦大学学院）；Department of Medical Physics, UCL（伦敦大学学院医学物理系）；Department of Neuroradiology（神经放射科）；St. Thomas’ Hospital（圣托马斯医院）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently …
Flinders University（弗林德斯大学）；Flinders Medical Centre（弗林德斯医疗中心）；Dr Jones & Partners Medical Imaging（琼斯博士及合伙人医疗影像中心）；University of Oxford（牛津大学）；University of Adelaide（阿德莱德大学）
Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any…
University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；University of California, Irvine（加利福尼亚大学欧文分校）
We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation. First, we utilize the ground-truth brain tumor lesions from a group of patients to generate the heatmaps of different types of lesions. These heatmaps are used to create the volume-of-interest (VOI) map which contains prior information about brain tumor lesions. The VOI map is then integrated with the multimodal MR images and input to a 3D U-Net for segmentation. The proposed method is evaluated on a public benchmark dataset, and the experimental results show that th…
King’s College London（伦敦国王学院）；National Hospital for Neurology and Neurosurgery（国家神经内科和神经外科医院）；Thomas Jefferson University（托马斯·杰斐逊大学）；University of Washington（华盛顿大学）
Laser interstitial thermal therapy (LITT) is a novel minimally invasive treatment that is used to ablate intracranial structures to treat mesial temporal lobe epilepsy (MTLE). Region of interest (ROI) segmentation before and after LITT would enable automated lesion quantification to objectively assess treatment efficacy. Deep learning techniques, such as convolutional neural networks (CNNs) are state-of-the-art solutions for ROI segmentation, but require large amounts of annotated data during the training. However, collecting large datasets from emerging treatments such as LITT is impractical.…
Multiple Sclerosis (MS) is a chronic neurological condition characterized by the development of lesions in the white matter of the brain. T2-fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Follow-up brain FLAIR MRI in MS provides helpful information for clinicians towards monitoring disease progression. In this study, we propose a novel modification to generative adversarial networks (GANs) to predict future lesion-specific FLAIR MRI for MS at fixed time inte…
