在这项工作中,我们报告了结合IEEE国际生物医学成像研讨会(ISBI)2016和国际医学影像计算机辅助干预会议(MICCAI)2017年组织的肝肿瘤分割基准(LITS)的设置和结果。将24种有效的最先进的肝脏和肝脏肿瘤分段算法应用于一组131个计算机断层扫描(CT)体积,具有不同类型的肿瘤对比度水平(高强度/低强度),组织异常(转移瘤)大小和不同程度的病变。已提交的算法已在70个未公开的卷上进行了测试。该数据集是与七家医院和研究机构合作创建的,由三位独立的放射科医师手动审查。我们发现没有一种算法对肝脏和肿瘤表现最佳。最佳肝脏分割算法的Dice评分为0.96(MICCAI),而对于肿瘤分割,最佳算法评估为0.67(ISBI)和0.70(MICCAI)。 LITS图像数据和手动注释继续通过在线评估系统公开提供,作为持续的基准测试资源。
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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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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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在这项工作中,我们通过两个CNN的级联来解决脑肿瘤分割问题,这两个CNN受到V-Net架构的启发\引用{VNet},重新构建残余连接并利用ROI掩模来限制网络仅限于相关的体素。这种架构允许通过仅将训练集中在肿瘤区域的真实性上,对具有高度偏斜的类别分布(例如脑肿瘤分类)的问题进行密集训练。 Weport在BraTS2017培训和验证集上的结果。
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RUNNING TITLE: In vivo EGFRvIII detection in glioblastoma via MRI signature
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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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白质病变和深灰质结构的分割是多发性硬化中磁共振成像量化的重要任务。通常这些任务是分开执行的:在本文中,我们提出了一个基于CNN的分段解决方案,用于快速,可靠地将多模态MR图像分割为病变类和健康的灰色和白质结构。与先前的方法相比,我们在骰子系数和病变特异性和敏感性方面显示出显着的,统计学上显着的改善,并且在人类内部评估者范围内与个体人类评价者协商。该方法是针对从单个中心收集的数据进行训练的:尽管如此,它对来自训练数据集中未表示的中心,扫描仪和场强的数据表现良好。一项回顾性研究发现,分类器成功识别出人类遗漏的病变。损伤标签由人类评估者提供,而其他脑结构(包括脑脊液,皮质灰质,皮质白质,小脑,扁桃体,海马,皮质下GM结构和脉络膜复合体)的弱标签由Freesurfer 5.3提供。这些结构的分割不仅与Freesurfer 5.3相当,而且与FSL-First和Freesurfer 6.1相比也很好。
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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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如Brats年度挑战所示,分割肿瘤及其子区域是一项具有挑战性的任务。此外,主要使用成像特征来预测患者的存活,同时作为评估患者治疗的理想结果,这也是一项艰巨的任务。在本文中,我们提出了一个级联管道来分割肿瘤及其子区域,然后我们将这些结果和其他临床特征与来自预训练VGG-16网络的图像特征结合起来,以预测患者的生存。通过培训和验证获得初步结果数据集在分割方面显示了一个有希望的开始,而通过对网络的特征提取部分的进一步测试可以改进预测值。
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Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.
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在本文中,我们提出了一种同时分割脑肿瘤的方法和一套广泛的风险器官,用于放射治疗计划胶质母细胞瘤。该方法将用于全脑分割的对比度自适应生成模型与使用卷积限制Boltzmann机器的肿瘤形状的新空间正则化模型相结合。我们实验证明,该方法能够适应图像采集,这种图像采集基本上不受任何可用的训练数据的影响,确保其在治疗部位的适用性;其肿瘤分割准确度可与现有技术水平相媲美;并且它足以很好地捕获风险中的大多数人用于放射治疗计划目的。所提出的方法可以是朝向在进行放射治疗的胶质母细胞瘤患者中自动描绘脑肿瘤和风险器官的有价值的步骤。
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从多模MRI扫描中准确分割胶质瘤的不同亚区域,包括肿瘤周围水肿,坏死核心,增强和非增强肿瘤核心,在脑肿瘤的诊断,预后和治疗中具有重要的临床意义。然而,由于高度异质的外观和形状,子区域的分割是非常具有挑战性的。使用深度学习模型的近期发展已证明其在过去的几个脑分割挑战以及其他语义和医学图像分割问题中的有效性。大脑肿瘤分割中的大多数模型使用2D / 3D贴片来预测中心体素的类别标签,并且使用变体尺寸和尺度来改善模型性能。然而,它具有低计算效率并且还具有有限的感受野。 U-Net是用于端到端分段的广泛使用的网络结构,可用于整个图像或提取的补丁,以在整个输入体素上提供分类标签,从而使其更有效并且期望在更大的输入尺寸下产生更好的性能。此外,不是选择最佳网络结构,而是在不同数据集不同超参数上训练的多个模型的集合通常可以改善分段性能。在这项研究中,我们建议使用具有不同超参数的3D U-Nets集合进行脑肿瘤分割。初步结果表明该模型的有效性。此外,我们使用提取的成像和非成像特征开发了用于生存预测的线性模型,尽管简单,但可以有效地减少过度拟合和回归误差。
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To facilitate a more widespread use of volumetric tumor segmentation in clinical studies, there is an urgent need for reliable, user-friendly segmentation software. The aim of this study was therefore to compare three different software packages for semi-automatic brain tumor segmentation of glioblastoma; namely BrainVoyager TM QX, ITK-Snap and 3D Slicer, and to make data available for future reference. Pre-operative, contrast enhanced T 1-weighted 1.5 or 3 Tesla Magnetic Resonance Imaging (MRI) scans were obtained in 20 consecutive patients who underwent surgery for glioblastoma. MRI scans were segmented twice in each software package by two investigators. Intra-rater, inter-rater and between-software agreement was compared by using differences of means with 95% limits of agreement (LoA), Dice's similarity coefficients (DSC) and Hausdorff distance (HD). Time expenditure of segmentations was measured using a stopwatch. Eighteen tumors were included in the analyses. Inter-rater agreement was highest for BrainVoyager with difference of means of 0.19 mL and 95% LoA from-2.42 mL to 2.81 mL. Between-software agreement and 95% LoA were very similar for the different software packages. Intra-rater, inter-rater and between-software DSC were ! 0.93 in all analyses. Time expenditure was approximately 41 min per segmentation in BrainVoyager, and 18 min per segmentation in both 3D Slicer and ITK-Snap. Our main findings were that there is a high agreement within and between the software packages in terms of small intra-rater, inter-rater and between-software differences of means and high Dice's similarity coefficients. Time expenditure was highest for BrainVoyager, but all software packages were relatively time-consuming, which may limit usability in an everyday clinical setting.
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Rationale and Objectives: Quantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients. Materials and Methods: Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1-and T2-weighted magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input. Results: Twenty-six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface. Conclusion: Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.
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A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator's experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently .
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Radiographic endpoints including response and progression are important for the evaluation of new glioblas-toma therapies. The current RANO criteria was developed to overcome many of the challenges identified with previous guidelines for response assessment, however, significant challenges and limitations remain. The current recommendations build on the strengths of the current RANO criteria, while addressing many of these limitations. Modifications to the current RANO criteria include suggestions for volumetric response evaluation, use contrast enhanced T1 subtraction maps to increase lesion conspicuity, removal of qualitative non-enhancing tumor assessment requirements, use of the post-radiation time point as the baseline for newly diagnosed glio-blastoma response assessment, and Btreatment-agnostic^ response assessment rubrics for identifying pseudoprogression, pseudoresponse, and a confirmed durable response in newly diagnosed and recurrent glioblastoma trials.
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The remarkable heterogeneity of glioblastoma, across patients and over time, is one of the main challenges in precision diagnostics and treatment planning. Non-invasive in vivo characterization of this heterogeneity using imaging could assist in understanding disease subtypes, as well as in risk-stratification and treatment planning of glioblastoma. The current study leveraged advanced imaging analytics and radiomic approaches applied to multi-parametric MRI of de novo glioblastoma patients (n = 208 discovery, n = 53 replication), and discovered three distinct and reproducible imaging subtypes of glioblastoma, with differential clinical outcome and underlying molecular characteristics, including isocitrate dehydrogenase-1 (IDH1), O 6-methylguanine-DNA methyltransferase, epidermal growth factor receptor variant III (EGFRvIII), and transcriptomic subtype composition. The subtypes provided risk-stratification substantially beyond that provided by WHO classifications. Within IDH1-wildtype tumors, our subtypes revealed different survival (p < 0.001), thereby highlighting the synergistic consideration of molecular and imaging measures for prognostication. Moreover, the imaging characteristics suggest that subtype-specific treatment of peritumoral infiltrated brain tissue might be more effective than current uniform standard-of-care. Finally, our analysis found subtype-specific radiogenomic signatures of EGFRvIII-mutated tumors. The identified subtypes and their clinical and molecular correlates provide an in vivo portrait of phenotypic heterogeneity in glioblastoma, which points to the need for precision diagnostics and personalized treatment. Precision diagnostics, prognostication, and personalized treatment in cancer patients call for finer characterization of tumors than current practice. Multi-parametric magnetic resonance imaging (mpMRI) is a powerful diagnostic tool that can facilitate in vivo characterization of diverse aspects of the tumor and its micro-environment 1,2. In this study, we aimed to characterize the heterogeneity of glioblastoma, which is the most aggressive adult
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