为了实现良好的性能和概括性,医疗图像分割模型应在具有足够可变性的大量数据集上进行培训。由于道德和治理限制以及与标签数据相关的成本,经常对科学发展进行扼杀,并经过对有限数据的培训和测试。数据增强通常用于人为地增加数据分布的可变性并提高模型的通用性。最近的作品探索了图像合成的深层生成模型,因为这种方法将使有效的无限数据生成多种多样的数据,从而解决了通用性和数据访问问题。但是,许多提出的解决方案限制了用户对生成内容的控制。在这项工作中,我们提出了Brainspade,该模型将基于合成扩散的标签发生器与语义图像发生器结合在一起。我们的模型可以在有或没有感兴趣的病理的情况下产生完全合成的大脑标签,然后产生任意引导样式的相应MRI图像。实验表明,Brainspade合成数据可用于训练分割模型,其性能与在真实数据中训练的模型相当。
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基于深度学习的疾病检测和分割算法承诺提高许多临床过程。然而,由于数据隐私,法律障碍和非统一数据采集协议,此类算法需要大量的注释训练数据,通常在医学环境中不可用。具有注释病理学的合成数据库可以提供所需的培训数据量。我们展示了缺血性卒中的例子,即利用基于深度学习的增强的病变分割的改善是可行的。为此,我们训练不同的图像到图像转换模型,以合成大脑体积的磁共振图像,并且没有来自语义分割图的中风病变。此外,我们培养一种生成的对抗性网络来产生合成病变面具。随后,我们组合这两个组件来构建大型合成描边图像数据库。使用U-NET评估各种模型的性能,该U-NET在临床测试集上培训以进行段中风病变。我们向最佳性能报告$ \ mathbf {72.8} $%[$ \ mathbf {70.8 \ pm1.0} $%]的骰子分数,这胜过了单独临床图像培训的模型培训$ \ mathbf { 67.3} $%[$ \ mathbf {63.2 \ pm1.9} $%],并且接近人类互相互联网骰子评分$ \ mathbf {76.9} $%。此外,我们表明,对于仅为10或50个临床案例的小型数据库,与使用不使用合成数据的设置相比,合成数据增强产生了显着的改进。据我们所知,这提出了基于图像到图像翻译的合成数据增强的第一个比较分析,并将第一应用于缺血性卒中。
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尽管数据增强和转移学习有所进步,但卷积神经网络(CNNS)难以推广到看不见的域。在分割大脑扫描时,CNN对分辨率和对比度的变化非常敏感:即使在相同的MRI模式内,则性能可能会跨数据集减少。在这里,我们介绍了Synthseg,第一个分段CNN无关紧要对比和分辨率。 Synthseg培训,用从分段上的生成模型采样的合成数据培训。至关重要,我们采用域随机化策略,我们完全随机开启了合成培训数据的对比度和解决。因此,Synthseg可以在没有再培训或微调的情况下对任何目标结构域进行真实扫描,这是首次分析大量的异构临床数据。因为Synthseg仅需要进行培训(无图像),所以它可以从通过不同群体的对象(例如,老化和患病)的自动化方法获得的标签中学习,从而实现广泛的形态变异性的鲁棒性。我们展示了Synthseg在六种方式的5,300扫描和十项决议中,与监督CNN,最先进的域适应和贝叶斯分割相比,它表现出无与伦比的泛化。最后,我们通过将其施加到心脏MRI和CT分割来证明SyntheeG的恒定性。
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生成的对抗网络(GAN)是在众多领域成功使用的一种强大的深度学习模型。它们属于一个称为生成方法的更广泛的家族,该家族通过从真实示例中学习样本分布来生成新数据。在临床背景下,与传统的生成方法相比,GAN在捕获空间复杂,非线性和潜在微妙的疾病作用方面表现出增强的能力。这篇综述评估了有关gan在各种神经系统疾病的成像研究中的应用的现有文献,包括阿尔茨海默氏病,脑肿瘤,脑老化和多发性硬化症。我们为每个应用程序提供了各种GAN方法的直观解释,并进一步讨论了在神经影像学中利用gans的主要挑战,开放问题以及有希望的未来方向。我们旨在通过强调如何利用gan来支持临床决策,并有助于更好地理解脑部疾病的结构和功能模式,从而弥合先进的深度学习方法和神经病学研究之间的差距。
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Clinical diagnostic and treatment decisions rely upon the integration of patient-specific data with clinical reasoning. Cancer presents a unique context that influence treatment decisions, given its diverse forms of disease evolution. Biomedical imaging allows noninvasive assessment of disease based on visual evaluations leading to better clinical outcome prediction and therapeutic planning. Early methods of brain cancer characterization predominantly relied upon statistical modeling of neuroimaging data. Driven by the breakthroughs in computer vision, deep learning became the de facto standard in the domain of medical imaging. Integrated statistical and deep learning methods have recently emerged as a new direction in the automation of the medical practice unifying multi-disciplinary knowledge in medicine, statistics, and artificial intelligence. In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation. The results do highlight that model-driven classical statistics and data-driven deep learning is a potent combination for developing automated systems in clinical oncology.
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人脑解剖图像的专家解释是神经放射学的中心部分。已经提出了几种基于机器学习的技术来协助分析过程。但是,通常需要对ML模型进行培训以执行特定的任务,例如脑肿瘤分割或分类。相应的培训数据不仅需要费力的手动注释,而且人脑MRI中可以存在多种异常 - 甚至同时发生,这使得所有可能的异常情况都非常具有挑战性。因此,可能的解决方案是一种无监督的异常检测(UAD)系统,可以从健康受试者的未标记数据集中学习数据分布,然后应用以检测​​分布样本。然后,这种技术可用于检测异常 - 病变或异常,例如脑肿瘤,而无需明确训练该特定病理的模型。过去已经为此任务提出了几种基于变异的自动编码器(VAE)技术。即使它们在人为模拟的异常情况下表现良好,但其中许多在检测临床数据中的异常情况下表现较差。这项研究提出了“上下文编码” VAE(CEVAE)模型的紧凑版本,并结合了预处理和后处理步骤,创建了UAD管道(Strega)(Strega),该步骤对临床数据更强大,并显示其在检测到其检测方面的适用性脑MRI中的肿瘤等异常。 The proposed pipeline achieved a Dice score of 0.642$\pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$\pm$0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522$\pm$0.135 and 0.783$\ PM分别为0.111美元。
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可以使用医学成像数据研究人类解剖学,形态和相关疾病。但是,访问医学成像数据受到治理和隐私问题,数据所有权和获取成本的限制,从而限制了我们理解人体的能力。解决此问题的一个可能解决方案是创建能够学习的模型,然后生成以相关性的特定特征(例如,年龄,性别和疾病状态)来生成人体的合成图像。最近,以神经网络形式的深层生成模型已被用于创建自然场景的合成2D图像。尽管如此,数据稀缺性,算法和计算局限性仍阻碍了具有正确解剖形态的高分辨率3D体积成像数据的能力。这项工作提出了一个生成模型,可以缩放以产生人类大脑的解剖学正确,高分辨率和现实的图像,并具有必要的质量,以允许进一步的下游分析。产生潜在无限数据的能力不仅能够对人体解剖学和病理学进行大规模研究,而不会危及患者的隐私,而且还可以在异常检测,模态综合,有限的数据和公平和公平和公平和公平和公平和公平和公平和公平和公平和公平和公平和公平和公平的学习领域进行显着提高。道德AI。代码和训练有素的模型可在以下网址提供:https://github.com/amigolab/synthanatomy。
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Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy preserving artificial intelligence and can also be used to augment small datasets. Here we show that diffusion probabilistic models can synthesize high quality medical imaging data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography (CT) images. We provide quantitative measurements of their performance through a reader study with two medical experts who rated the quality of the synthesized images in three categories: Realistic image appearance, anatomical correctness and consistency between slices. Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0.91 vs. 0.95 without vs. with synthetic data).
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医疗图像合成引起了人们的关注,因为它可能会产生缺失的图像数据,改善诊断并受益于许多下游任务。但是,到目前为止,开发的合成模型并不适应显示域移位的看不见的数据分布,从而限制了其在临床常规中的适用性。这项工作着重于探索3D图像到图像合成模型的域适应性(DA)。首先,我们强调了分类,分割和合成模型之间DA的技术差异。其次,我们提出了一种基于近似3D分布的2D变异自动编码器的新型有效适应方法。第三,我们介绍了有关适应数据量和关键超参数量的影响的经验研究。我们的结果表明,所提出的方法可以显着提高3D设置中未见域的合成精度。该代码可在https://github.com/winstonhutiger/2d_vae_uda_for_3d_sythesis上公开获得。
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Many clinical and research studies of the human brain require an accurate structural MRI segmentation. While traditional atlas-based methods can be applied to volumes from any acquisition site, recent deep learning algorithms ensure very high accuracy only when tested on data from the same sites exploited in training (i.e., internal data). The performance degradation experienced on external data (i.e., unseen volumes from unseen sites) is due to the inter-site variabilities in intensity distributions induced by different MR scanner models, acquisition parameters, and unique artefacts. To mitigate this site-dependency, often referred to as the scanner effect, we propose LOD-Brain, a 3D convolutional neural network with progressive levels-of-detail (LOD) able to segment brain data from any site. Coarser network levels are responsible to learn a robust anatomical prior useful for identifying brain structures and their locations, while finer levels refine the model to handle site-specific intensity distributions and anatomical variations. We ensure robustness across sites by training the model on an unprecedented rich dataset aggregating data from open repositories: almost 27,000 T1w volumes from around 160 acquisition sites, at 1.5 - 3T, from a population spanning from 8 to 90 years old. Extensive tests demonstrate that LOD-Brain produces state-of-the-art results, with no significant difference in performance between internal and external sites, and robust to challenging anatomical variations. Its portability opens the way for large scale application across different healthcare institutions, patient populations, and imaging technology manufacturers. Code, model, and demo are available at the project website.
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创伤性脑损伤(TBI)患者的脑网络分析对于其意识水平评估和预后评估至关重要,这需要分割某些意识相关的大脑区域。但是,由于很难收集TBI患者的手动注释的MR扫描,因此很难构建TBI分割模型。数据增强技术可用于缓解数据稀缺问题。但是,常规数据增强策略(例如空间和强度转化)无法模仿创伤性大脑中的变形和病变,这限制了后续分割任务的性能。为了解决这些问题,我们提出了一种名为TBIGA的新型医学图像授课模型,以通过配对的脑标签图合成TBI MR扫描。我们的TBIGAN方法的主要优势在于,它可以同时生成TBI图像和相应的标签映射,这在以前的医学图像的先前涂上方法中尚未实现。我们首先按照粗到细节的方式在边缘信息的指导下生成成分的图像,然后将合成强度图像用作标签上填充的先验。此外,我们引入了基于注册的模板增强管道,以增加合成图像对的多样性并增强数据增强能力。实验结果表明,提出的TBIGAN方法可以产生具有高质量和有效标签图的足够合成的TBI图像,这可以大大改善与替代方案相比的2D和3D创伤性脑部分割性能。
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多发性硬化症(MS)是中枢神经系统的慢性炎症和退行性疾病,其特征在于,白色和灰质的外观与个体患者的神经症状和标志进行地平整相关。磁共振成像(MRI)提供了详细的体内结构信息,允许定量和分类MS病变,其批判性地通知疾病管理。传统上,MS病变在2D MRI切片上手动注释,一个流程效率低,易于观察室内误差。最近,已经提出了自动统计成像分析技术以基于MRI体素强度检测和分段段病变。然而,它们的有效性受到MRI数据采集技术的异质性和MS病变的外观的限制。通过直接从图像学习复杂的病变表现,深度学习技术已经在MS病变分割任务中取得了显着的突破。在这里,我们提供了全面审查最先进的自动统计和深度学习MS分段方法,并讨论当前和未来的临床应用。此外,我们审查了域适应等技术策略,以增强现实世界临床环境中的MS病变分段。
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Automated medical image segmentation using deep neural networks typically requires substantial supervised training. However, these models fail to generalize well across different imaging modalities. This shortcoming, amplified by the limited availability of annotated data, has been hampering the deployment of such methods at a larger scale across modalities. To address these issues, we propose M-GenSeg, a new semi-supervised training strategy for accurate cross-modality tumor segmentation on unpaired bi-modal datasets. Based on image-level labels, a first unsupervised objective encourages the model to perform diseased to healthy translation by disentangling tumors from the background, which encompasses the segmentation task. Then, teaching the model to translate between image modalities enables the synthesis of target images from a source modality, thus leveraging the pixel-level annotations from the source modality to enforce generalization to the target modality images. We evaluated the performance on a brain tumor segmentation datasets composed of four different contrast sequences from the public BraTS 2020 challenge dataset. We report consistent improvement in Dice scores on both source and unannotated target modalities. On all twelve distinct domain adaptation experiments, the proposed model shows a clear improvement over state-of-the-art domain-adaptive baselines, with absolute Dice gains on the target modality reaching 0.15.
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Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019.
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深度神经网络在医学图像分析中带来了显着突破。但是,由于其渴望数据的性质,医学成像项目中适度的数据集大小可能会阻碍其全部潜力。生成合成数据提供了一种有希望的替代方案,可以补充培训数据集并进行更大范围的医学图像研究。最近,扩散模型通过产生逼真的合成图像引起了计算机视觉社区的注意。在这项研究中,我们使用潜在扩散模型探索从高分辨率3D脑图像中生成合成图像。我们使用来自英国生物银行数据集的T1W MRI图像(n = 31,740)来训练我们的模型,以了解脑图像的概率分布,该脑图像以协变量为基础,例如年龄,性别和大脑结构量。我们发现我们的模型创建了现实的数据,并且可以使用条件变量有效地控制数据生成。除此之外,我们创建了一个带有100,000次脑图像的合成数据集,并使科学界公开使用。
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这项工作提出了一个新颖的框架CISFA(对比图像合成和自我监督的特征适应),该框架建立在图像域翻译和无监督的特征适应性上,以进行跨模式生物医学图像分割。与现有作品不同,我们使用单方面的生成模型,并在输入图像的采样贴片和相应的合成图像之间添加加权贴片对比度损失,该图像用作形状约束。此外,我们注意到生成的图像和输入图像共享相似的结构信息,但具有不同的方式。因此,我们在生成的图像和输入图像上强制实施对比损失,以训练分割模型的编码器,以最大程度地减少学到的嵌入空间中成对图像之间的差异。与依靠对抗性学习进行特征适应的现有作品相比,这种方法使编码器能够以更明确的方式学习独立于域的功能。我们对包含腹腔和全心的CT和MRI图像的分割任务进行了广泛评估。实验结果表明,所提出的框架不仅输出了较小的器官形状变形的合成图像,而且还超过了最先进的域适应方法的较大边缘。
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Deep Learning models are easily disturbed by variations in the input images that were not seen during training, resulting in unpredictable behaviours. Such Out-of-Distribution (OOD) images represent a significant challenge in the context of medical image analysis, where the range of possible abnormalities is extremely wide, including artifacts, unseen pathologies, or different imaging protocols. In this work, we evaluate various uncertainty frameworks to detect OOD inputs in the context of Multiple Sclerosis lesions segmentation. By implementing a comprehensive evaluation scheme including 14 sources of OOD of various nature and strength, we show that methods relying on the predictive uncertainty of binary segmentation models often fails in detecting outlying inputs. On the contrary, learning to segment anatomical labels alongside lesions highly improves the ability to detect OOD inputs.
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每年都会在医院中获得数百万个大脑MRI扫描,这比任何研究数据集的规模都要大得多。因此,分析此类扫描的能力可以改变神经成像研究。然而,由于没有自动化算法可以应对临床采集的高度可变性(MR对比度,分辨率,方向等),因此它们的潜力仍未开发。在这里,我们提出了Synthseg+,这是一个AI分割套件,首次可以对异质临床数据集进行强有力的分析。具体而言,除了全脑分割外,SynthSeg+还执行皮质细胞,颅内体积估计和自动检测故障分割(主要是由质量非常低的扫描引起的)。我们在七个实验中证明了合成++,包括对14,000张扫描的老化研究,在该研究中,它准确地复制了在质量更高的数据上观察到的萎缩模式。 Synthseg+公开发布是一种现成的工具,可在广泛设置中解锁定量形态计量学的潜力。
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了解脑损伤的强度特征是定义神经系统研究和预测疾病负担和结局的基于图像的生物标志物的关键。在这项工作中,我们提出了一种基于前景的新型生成方法,用于对局部病变特征进行建模,该方法既可以在健康图像上产生合成病变,又可以从病理图像中综合受试者特异性的伪健康图像。此外,该方法可以用作数据增强模块,以生成用于训练大脑图像分割网络的合成图像。在磁共振成像(MRI)上获得的多发性硬化症(MS)脑图像的实验表明,所提出的方法可以生成高度逼真的伪健康和伪病理学脑图像。与传统的数据增强方法以及最近的病变感知数据增强技术Carvemix相比,使用合成图像进行数据扩展可改善大脑图像分割的性能。该代码将在https://github.com/dogabasaran/lesion-synthesis中发布。
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甚至在没有受限,监督的情况下,也提出了甚至在没有受限或有限的情况下学习普遍陈述的方法。使用适度数量的数据可以微调新的目标任务,或者直接在相应任务中实现显着性能的无奈域中使用的良好普遍表示。这种缓解数据和注释要求为计算机愿景和医疗保健的应用提供了诱人的前景。在本辅导纸上,我们激励了对解散的陈述,目前关键理论和详细的实际构建块和学习此类表示的标准的需求。我们讨论医学成像和计算机视觉中的应用,强调了在示例钥匙作品中进行的选择。我们通过呈现剩下的挑战和机会来结束。
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