Neuroimaging in the context of stroke is becoming more and more important. Quantifying and characterizing stroke lesions is still an open challenge. In this paper, we propose a novel framework to solve this problem. The features we use are intensities of patches from multiscale multimodal magnetic resonance (MR) images. We have built random forest classifiers for different parts of the whole brain. A leave-one-out cross-validation result on SISS training data yields 0.55 in Dice score. Abstract. We present our 11-layers deep, double-pathway, 3D Convo-lutional Neural Network, developed for the segmentation of brain lesions. The developed system segments pathology voxel-wise after processing a corresponding multi-modal 3D patch at multiple scales. We demonstrate that it is possible to train such a deep and wide 3D CNN on a small dataset of 28 cases. Our network yields promising results on the task of segmenting ischemic stroke lesions, accomplishing a mean Dice of 64% (66% after postprocessing) on the ISLES 2015 training dataset, ranking among the top entries. Regardless its size, our network is capable of processing a 3D brain volume in 3 minutes, making it applicable to the automated analysis of larger study cohorts. Abstract. Stroke is a common cause of sudden death and disability worldwide. In clinical practice, brain magnetic resonance (MR) scans are used to assess the stroke lesion presence. In this work, we have built a fully automatic stroke lesion segmentation system using 3D brain magnetic resonance (MR) data. The system contains a 3D registration framework and a 3D multi-random forest model trained from the data provided by the Ischemic Stroke Lesion Segmentation (ISLES) challenge of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. The preliminary test results show that the presented system is capable to detect stroke lesion from 3D brain MRI data. Abstract. This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-spectral MRI images. The segmentation framework is validated on MICCAI 2015 ISLES challenge training data sets. The performance of the framework is evaluated relative to the manual segmentation (ground truth). The experimental results demonstrate the robustness of the seg-mentation framework, and that it achieves reasonable segmentation accuracy for segmenting the sub-acute ischemic stroke lesion in multi-spectral MRI images. Abstract. Deep Neural Networks (DNNs) are often successful at solving problems for which useful high-level features are not obvious to design. This document presents how DNNs can be used for autom
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Neuroimaging in the context of stroke is becoming more and more important. Quantifying and characterizing stroke lesions is still an open challenge. In this paper, we propose a novel framework to solve this problem. The features we use are intensities of patches from multiscale multimodal magnetic resonance (MR) images. We have built random forest classifiers for different parts of the whole brain. A leave-one-out cross-validation result on SISS training data yields 0.55 in Dice score. Abstract. We present our 11-layers deep, double-pathway, 3D Convo-lutional Neural Network, developed for the segmentation of brain lesions. The developed system segments pathology voxel-wise after processing a corresponding multi-modal 3D patch at multiple scales. We demonstrate that it is possible to train such a deep and wide 3D CNN on a small dataset of 28 cases. Our network yields promising results on the task of segmenting ischemic stroke lesions, accomplishing a mean Dice of 64% (66% after postprocessing) on the ISLES 2015 training dataset, ranking among the top entries. Regardless its size, our network is capable of processing a 3D brain volume in 3 minutes, making it applicable to the automated analysis of larger study cohorts. Abstract. Stroke is a common cause of sudden death and disability worldwide. In clinical practice, brain magnetic resonance (MR) scans are used to assess the stroke lesion presence. In this work, we have built a fully automatic stroke lesion segmentation system using 3D brain magnetic resonance (MR) data. The system contains a 3D registration framework and a 3D multi-random forest model trained from the data provided by the Ischemic Stroke Lesion Segmentation (ISLES) challenge of the 18th International Conference on Medical Image Computing and Computer Assisted Intervention. The preliminary test results show that the presented system is capable to detect stroke lesion from 3D brain MRI data. Abstract. This paper presents an automated segmentation framework for ischemic stroke lesion segmentation in multi-spectral MRI images. The framework is based on a random forests (RF), which is an ensemble learning technique that generates several classifiers and combines their results in order to make decisions. In RF, we employ several meaningful features such as intensities, entropy, gradient etc. to classify the voxels in multi-spectral MRI images. The segmentation framework is validated on MICCAI 2015 ISLES challenge training data sets. The performance of the framework is evaluated relative to the manual segmentation (ground truth). The experimental results demonstrate the robustness of the seg-mentation framework, and that it achieves reasonable segmentation accuracy for segmenting the sub-acute ischemic stroke lesion in multi-spectral MRI images. Abstract. Deep Neural Networks (DNNs) are often successful at solving problems for which useful high-level features are not obvious to design. This document presents how DNNs can be used for autom
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白质高信号(WMH)常见于健康老年人的大脑中,并与各种神经系统老年病有关。在本文中,我们提出了一个使用深度完全卷积网络和集合模型来自动检测这种WMHusing流体衰减反转恢复(FLAIR)和T1磁共振(MR)扫描的研究。该算法在2017年MICCAI的WMH分段挑战中进行了评估和排名第一。在评估阶段,该算法的实施被提交给挑战组织者,然后挑战组织者在5个扫描仪的110个案例中隐藏了一组。在保持的测试数据集上获得的平均骰子得分,精度和稳健的Hausdorff距离分别为80%,84%和6.30mm。这些是在挑战中取得的最高成绩,表明所提出的方法是最先进的。在本文中,我们提供了系统关键组件的详细描述和定量分析。此外,还提出了跨扫描器评估的研究,以讨论模态和数据增强的组合如何影响系统的泛化能力。还研究了系统对不同扫描仪和协议的适应性。进一步提出定量研究以测试整体尺寸的影响。此外,我们的方法的软件和模型是公开的。所提出的系统的有效性和泛化能力显示其对于现实世界临床实践的潜力。
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Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of large-scale medical trials and quantitative image analyses. We train and cascade two FCNs for the combined segmentation of the liver and its lesions. As a first step, we train an FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validation results on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for the liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on 38 MRI liver tumor volumes and the public 3DIRCAD dataset.
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Variations in the shape and appearance of anatomical structures in medical images are often relevant radiological signs of disease. Automatic tools can help automate parts of this manual process. A cloud-based evaluation framework is presented in this paper including results of benchmarking current state-of-the-art medical imaging algorithms for anatomical structure segmentation and landmark detection: the VISCERAL Anatomy benchmarks. The algorithms are implemented in virtual machines in the cloud where participants can only access the training data and can be run privately by the benchmark administrators to objectively compare their performance in an unseen common test set. Overall, 120 computed tomography and magnetic resonance patient volumes were manually annotated to create a standard Gold Corpus containing a total of 1295 structures and 1760 landmarks. Ten participants contributed with automatic algorithms for the organ segmentation task, and three for the landmark localization task. Different algorithms obtained the best scores in the four available imaging modalities and for subsets of anatomical structures. The annotation framework, resulting data set, evaluation setup, results and performance analysis from the three VISCERAL Anatomy benchmarks are presented in this article. Both the VISCERAL data set and Silver Corpus generated with the fusion of the participant algorithms on a larger set of non-manually-annotated medical images are available to the research community.
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In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: 1) the sharing of a rich data set; 2) collaboration and comparison of the various avenues of research being pursued in the community; and 3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 1 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
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Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.
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肝癌是导致癌症死亡的主要原因之一。为了帮助医生进行肝细胞癌的诊断和治疗计划,在临床实践中需要一种准确的自动肝脏和肿瘤分割方法。最近,完全卷积神经网络(FCN),包括2D和3D FCN,在许多体积图像分割中充当了骨干。然而,2D卷积不能充分利用沿第三维的空间信息,而3D卷绕遭受高计算成本和GPU内存消耗。为了解决这些问题,我们提出了一种新颖的混合密集连接的UNet(H-DenseUNet),它由2D DenseUNet组成,用于有效地提取片内特征,以及3Dcounterpart,用于在肝脏和肿瘤的自动上下文算法的精神下分层聚集体积上下文。分割。我们以端到端的方式制定H-DenseUNet的学习过程,其中可以通过混合特征融合(HFF)层联合优化切片表示和片间特征。我们对MICCAI 2017肝肿瘤分割(LiTS)挑战和3DIRCADb数据集的数据集进行了广泛的评估。我们的方法在肿瘤的分割结果方面优于其他现有技术,并且即使使用单一模型也能实现非常有竞争力的分割效果。
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Magnetic resonance (MR) imaging is often used to characterize and quantify multiple sclerosis (MS) lesions in the brain and spinal cord. The number and volume of lesions have been used to evaluate MS disease burden, to track the progression of the disease and to evaluate the effect of new pharmaceuticals in clinical trials. Accurate identification of MS lesions in MR images is extremely difficult due to variability in lesion location, size and shape in addition to anatomical variability between subjects. Since manual segmentation requires expert knowledge, is time consuming and is subject to intra-and inter-expert variability, many methods have been proposed to automatically segment lesions. The objective of this study was to carry out a systematic review of the literature to evaluate the state of the art in automated multiple sclerosis lesion segmentation. From 1240 hits found initially with PubMed and Google scholar, our selection criteria identified 80 papers that described an automatic lesion segmentation procedure applied to MS. Only 47 of these included quantitative validation with at least one realistic image. In this paper, we describe the complexity of lesion segmentation, classify the automatic MS lesion segmentation methods found, and review the validation methods applied in each of the papers reviewed. Although many segmentation solutions have been proposed, including some with promising results using MRI data obtained on small groups of patients, no single method is widely employed due to performance issues related to the high variability of MS lesion appearance and differences in image acquisition. The challenge remains to provide segmentation techniques that work in all cases regardless of the type of MS, duration of the disease, or MRI protocol, and this within a comprehensive, standardized validation framework. MS lesion segmentation remains an open problem.
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Highlights • An efficient 11-layers deep, multi-scale, 3D CNN architecture. • A novel training strategy that significantly boosts performance. • The first employment of a 3D fully connected CRF for post-processing. • State-of-the-art performance on three challenging lesion segmentation tasks. • New insights into the automatically learned intermediate representations. Abstract We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of lesion segmentation in multi-channel MRI patient data with traumatic brain injuries, brain tumors, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computation-ally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
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脊髓经常受到多发性硬化(MS)患者的萎缩和/或损伤的影响。来自MRI数据的脊髓和病变的分割提供了损伤的测量,这是MS的诊断,预后和纵向监测的关键标准。自动化此操作可消除评估者之间的差异,并提高大吞吐量分析管道的效率。由于与采集参数和图像伪像相关的大的变异性,跨多位点脊髓数据的稳健且可靠的分割是具有挑战性的。本研究的目的是开发一种全自动框架,对图像参数和临床条件的可变性具有鲁棒性,可用于从传统MRI数据中分割脊髓和髓内MS病变。在这项多地点研究中纳入了1,042名受试者(459名健康对照,471名MS患者和112名其他脊柱病变)的扫描(n = 30)。数据跨越三个对比(T1-,T2-和T2 * - 加权),共计1,943卷。所提出的脐带病变自动分割方法基于两个卷积神经网络(CNN)的序列。为了处理非常小比例的脊髓和/或病变体素与体积的其余部分相比,具有2D扩张卷曲的第一CNN检测脊髓中心线,接着是第二CNN,其具有分割脊髓和/或脊髓的3D卷曲。与手动分割相比,我们基于CNN的方法显示中位数骰子为95%而PropSeg为88%,这是一种先进的脊髓分割方法。关于MS数据的病变分割,我们的框架提供了60%的骰子,-15%的相对体积差异,以及83%和77%的病变检测灵敏度和精确度。建议的框架是开源的,可以在脊髓工具箱中随时使用。
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完全卷积的深度神经网络被认为是快速和精确的框架,在图像分割方面具有巨大的潜力。当数据不平衡时,利用这种网络的主要挑战之一就会增加,这在诸如病变分割的许多医学成像应用中是常见的,其中病变类体素的数量通常比非病变体素低得多。具有不平衡数据的训练网络可以进行具有高精度和低召回率的预测,严重偏向非病变类别,这在医学应用中是特别不希望的,其中假阴性实际上比假阳性更重要。已经提出了各种方法来解决该问题,包括两步训练,采样加权,平衡采样和相似性损失函数。在本文中,我们开发了一个具有非对称损失函数的拼接三维密集连接网络,其中我们使用大的重叠图像补丁进行内在的内部数据增强,补丁选择算法和基于B样条加权软投票的补丁预测融合策略inaccount补丁边界预测的不确定性。我们将此方法应用于基于MSSEG 2016和ISBI 2015挑战的病变分割,其中我们的平均Dice相似系数分别为69.9%和65.74%。除了提议的损失,我们训练我们的网络具有局部和广义的骰子丢失功能。使用非对称相似性损失层和我们的3D贴片预测融合,在测试中实现了$ F_1 $和$ F_2 $得分以及APR曲线的显着改善。基于$ F_ \ beta $得分的非对称相似性损失推广了Dice相似系数,并且可以有效地与开发的补丁策略一起用于训练完全卷积深度神经网络以进行高度不平衡图像分割。
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从CT体积中自动提取肝脏和肿瘤是一个具有挑战性的任务,因为它们的异质和扩散形状。最近,2D和3D深度卷积神经网络已经在医学图像分割技术中变得流行,因为利用大的标记数据集来学习分层特征。然而,3D网络由于其在计算资源上的高成本而具有一些缺点。在本文中,我们提出了一种名为RA-UNet的3D混合残留注意感知分割方法,以精确地提取肝脏感兴趣体积(VOI)和肝脏切片肿瘤。所提出的网络具有作为3D U-Net的基本架构,其提取将低级特征映射与高级特征映射相结合的背景信息。注意模块被堆叠以使得注意感知特征随着网络“非常深”而自适应地改变并且这通过残余学习成为可能。这是注意力剩余机制用于处理医学体积图像的第一项工作。我们在公共MICCAI 2017肝脏肿瘤分割数据集和3DIRCADb数据集上评估了我们的框架。结果表明我们的架构优于其他最先进的方法。我们还将我们的RA-UNet扩展到了Brats2018和BraTS2017数据集上的脑肿瘤分割,结果表明RA-UN还在脑肿瘤分割任务中具有良好的性能。
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近年来,深度卷积神经网络(CNN)已经在各种计算机视觉问题中表现出记录破坏性能,例如视觉对象识别,检测和分割。这些方法还用于医学图像分析领域,用于病变分割,解剖学分割和分类。我们提供了有关CNN技术应用于脑磁共振成像(MRI)分析的广泛文献综述,重点介绍了这些工作中可用的体系结构,预处理,数据准备和后处理策略。这项研究的目的是三倍。我们的主要目标是报告CNN架构的不同之处,讨论最先进的策略,使用公共数据集浓缩其结果并检查其优缺点。其次,本文旨在详细介绍深部CNN脑内MRI分析的研究活动。最后,我们对CNN的未来进行了展望,其中我们暗示了随后几年的一些研究方向。
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Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.
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Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.
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Brain extraction from magnetic resonance imaging (MRI) is crucial for many neuroimaging workflows. Current methods demonstrate good results on non-enhanced T1-weighted images, but struggle when confronted with other modalities and pathologically altered tissue. In this paper we present a 3D convolutional deep learning architecture to address these shortcomings. In contrast to existing methods, we are not limited to non-enhanced T1w images. When trained appropriately, our approach handles an arbitrary number of modalities including contrast-enhanced scans. Its applicability to MRI data, comprising four channels: non-enhanced and contrast-enhanced T1w, T2w and FLAIR contrasts, is demonstrated on a challenging clinical data set containing brain tumors (N = 53), where our approach significantly outperforms six commonly used tools with a mean Dice score of 95.19. Further, the proposed method at least matches state-of-the-art performance as demonstrated on three publicly available data sets: IBSR, LPBA40 and OASIS, totaling N = 135 volumes. For the IBSR (96.32) and LPBA40 (96.96) data set the convolutional neuronal network (CNN) obtains the highest average Dice scores, albeit not being significantly different from the second best performing method. For the OASIS data the second best Dice (95.02) results are achieved, with no statistical difference in comparison to the best performing tool. For all data sets the highest average specificity measures are evaluated, whereas the sensitivity displays about average results. Adjusting the cutoff threshold for generating the binary masks from the CNN's probability output can be used to increase the sensitivity of the method. Of course, this comes at the cost of a decreased specificity and has to be decided application specific. Using an optimized GPU implementation predictions can be achieved in less than one minute. The proposed method may prove useful for large-scale studies and clinical trials.
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