完全排列和配对的多模式神经成像数据的存在证明了其在诊断脑疾病中的有效性。但是，收集完整的一组良好的配对数据是不切实际的，因为实际困难可能包括高成本，长期获取，图像腐败和隐私问题。以前，未配对的神经影像数据（称为泥）通常被视为嘈杂的标签。但是，这种基于嘈杂的标签的方法在严重发生扭曲的数据时无法完成。例如，旋转角度不同。在本文中，我们提出了一种新的联邦自制学习（FEDMED），以用于脑形象合成。制定了仿射变换损失（ATL），以利用严重扭曲的图像，而无需违反医院的隐私立法。然后，我们引入了一种新的数据增强程序，以进行自我监督训练，并将其送入三个辅助头，即辅助旋转，辅助翻译和辅助缩放头。所提出的方法证明了在严重错误和未配对的数据设置下，我们合成结果的质量的高级性能，并且比其他基于GAN的算法更好。提出的方法还减少了对可变形注册的需求，同时鼓励利用未对准和未配对的数据。与其他最先进的方法相比，实验结果验证了我们学习范式的出色表现。

The existence of completely aligned and paired multi-modal neuroimaging data has proved its effectiveness in diagnosis of brain diseases. However, collecting the full set of well-aligned and paired data is expensive or even impractical, since the practical difficulties may include high cost, long time acquisition, image corruption, and privacy issues. A realistic solution is to explore either an unsupervised learning or a semi-supervised learning to synthesize the absent neuroimaging data. In this paper, we are the first one to comprehensively approach cross-modality neuroimage synthesis task from different perspectives, which include the level of the supervision (especially for weakly-supervised and unsupervised), loss function, evaluation metrics, the range of modality synthesis, datasets (aligned, private and public) and the synthesis-based downstream tasks. To begin with, we highlight several opening challenges for cross-modality neuroimage sysnthesis. Then we summarize the architecture of cross-modality synthesis under various of supervision level. In addition, we provide in-depth analysis of how cross-modality neuroimage synthesis can improve the performance of different downstream tasks. Finally, we re-evaluate the open challenges and point out the future directions for the remaining challenges. All resources are available at https://github.com/M-3LAB/awesome-multimodal-brain-image-systhesis

监督PIX2PIX和无监督的周期一致性是两个模式，主导医学图像到图像转换的领域。但是，两种模式都是理想的。 PIX2PIX模式具有出色的性能。但是它需要配对且良好的像素 - 明智的对齐图像，这可能并不总是可以实现由于获取配对图像的次数之间的呼吸运动或解剖学变化。循环一致性模式与训练数据不那么严格，并且在未配对或未对齐的图像上运行良好。但它的表现可能不是最佳的。为了打破现有模式的困境，我们提出了一种称为中文的新的无监督模式，用于医学图像到图像转换。它基于“损失校正”理论。在登录中，未对准的目标图像被认为是嘈杂的标签，并且发电机接受了额外的登记网络，以适应性地拟合未对准的噪声分布。目标是搜索图像到图像转换和注册任务的常见最佳解决方案。我们将登上regan纳入一些最先进的图像到图像形象翻译方法，并证明了Regan可以很容易地与这些方法结合，以改善他们的性能。如我们模式中简单的Cyclegan，即使使用较少的网络参数，也会超越最新的漂亮。根据我们的结果，Reggan以错位或未配对数据上的对齐数据和周期一致性的PIX2PIX两者都表现优惠。 Reggan对噪音不敏感，这使得它可以更好地选择各种场景，特别是对于医学图像到图像转换任务，其中不可用的井像素对齐数据

联合学习（FL）可用于通过使多个机构协作，改善磁共振（MR）图像重建的数据隐私和效率，而无需聚合本地数据。然而，由不同MR成像协议引起的域移位可以显着降低FL模型的性能。最近的流程倾向于通过增强全局模型的概括来解决这一点，但它们忽略了特定于域的特征，这可能包含有关设备属性的重要信息，并且对本地重建有用。在本文中，我们提出了一种针对MR图像重建（FEDMRI）的特异性保存流算法。核心思想是将MR重建模型划分为两个部分：全局共享编码器，以在全局级别获取概括的表示，以及客户特定的解码器，以保留每个客户端的特定于域的属性，这对于协作很重要当客户具有独特的分发时重建。此外，为了进一步提高全局共享编码器的收敛，当存在域移位时，引入加权对比正规化以在优化期间直接校正客户端和服务器之间的任何偏差。广泛的实验表明，我们的Fedmri的重建结果是最接近多机构数据的地面真理，并且它优于最先进的FL方法。

Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aroused gaining research interest to cope with this limitation. In this paper, we propose a bidirectional learning model, denoted as dual contrast cycleGAN (DC-cycleGAN), to synthesize medical images from unpaired data. Specifically, a dual contrast loss is introduced into the discriminators to indirectly build constraints between real source and synthetic images by taking advantage of samples from the source domain as negative samples and enforce the synthetic images to fall far away from the source domain. In addition, cross-entropy and structural similarity index (SSIM) are integrated into the DC-cycleGAN in order to consider both the luminance and structure of samples when synthesizing images. The experimental results indicate that DC-cycleGAN is able to produce promising results as compared with other cycleGAN-based medical image synthesis methods such as cycleGAN, RegGAN, DualGAN, and NiceGAN. The code will be available at https://github.com/JiayuanWang-JW/DC-cycleGAN.

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。

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.

Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more general case, the distributions of labels among clients are different, called ``label distribution skew''. Directly applying conventional federated learning without consideration of label distribution skew issue significantly hurts the performance of the global model. To this end, we propose a novel federated learning method, named FedMGD, to alleviate the performance degradation caused by the label distribution skew issue. It introduces a global Generative Adversarial Network to model the global data distribution without access to local datasets, so the global model can be trained using the global information of data distribution without privacy leakage. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art on several public benchmarks. Code is available at \url{https://github.com/Sheng-T/FedMGD}.

Cross-modality magnetic resonance (MR) image synthesis aims to produce missing modalities from existing ones. Currently, several methods based on deep neural networks have been developed using both source- and target-modalities in a supervised learning manner. However, it remains challenging to obtain a large amount of completely paired multi-modal training data, which inhibits the effectiveness of existing methods. In this paper, we propose a novel Self-supervised Learning-based Multi-scale Transformer Network (SLMT-Net) for cross-modality MR image synthesis, consisting of two stages, \ie, a pre-training stage and a fine-tuning stage. During the pre-training stage, we propose an Edge-preserving Masked AutoEncoder (Edge-MAE), which preserves the contextual and edge information by simultaneously conducting the image reconstruction and the edge generation. Besides, a patch-wise loss is proposed to treat the input patches differently regarding their reconstruction difficulty, by measuring the difference between the reconstructed image and the ground-truth. In this case, our Edge-MAE can fully leverage a large amount of unpaired multi-modal data to learn effective feature representations. During the fine-tuning stage, we present a Multi-scale Transformer U-Net (MT-UNet) to synthesize the target-modality images, in which a Dual-scale Selective Fusion (DSF) module is proposed to fully integrate multi-scale features extracted from the encoder of the pre-trained Edge-MAE. Moreover, we use the pre-trained encoder as a feature consistency module to measure the difference between high-level features of the synthesized image and the ground truth one. Experimental results show the effectiveness of the proposed SLMT-Net, and our model can reliably synthesize high-quality images when the training set is partially unpaired. Our code will be publicly available at https://github.com/lyhkevin/SLMT-Net.

Magnetic Resonance Fingerprinting (MRF) is an efficient quantitative MRI technique that can extract important tissue and system parameters such as T1, T2, B0, and B1 from a single scan. This property also makes it attractive for retrospectively synthesizing contrast-weighted images. In general, contrast-weighted images like T1-weighted, T2-weighted, etc., can be synthesized directly from parameter maps through spin-dynamics simulation (i.e., Bloch or Extended Phase Graph models). However, these approaches often exhibit artifacts due to imperfections in the mapping, the sequence modeling, and the data acquisition. Here we propose a supervised learning-based method that directly synthesizes contrast-weighted images from the MRF data without going through the quantitative mapping and spin-dynamics simulation. To implement our direct contrast synthesis (DCS) method, we deploy a conditional Generative Adversarial Network (GAN) framework and propose a multi-branch U-Net as the generator. The input MRF data are used to directly synthesize T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) images through supervised training on paired MRF and target spin echo-based contrast-weighted scans. In-vivo experiments demonstrate excellent image quality compared to simulation-based contrast synthesis and previous DCS methods, both visually as well as by quantitative metrics. We also demonstrate cases where our trained model is able to mitigate in-flow and spiral off-resonance artifacts that are typically seen in MRF reconstructions and thus more faithfully represent conventional spin echo-based contrast-weighted images.

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。

跨模式图像合成是一个主动研究主题，具有多个医学临床相关的应用。最近，允许对配对但未对准数据进行培训的方法开始出现。但是，没有适用于广泛的现实世界数据集的健壮且良好的方法。在这项工作中，我们通过引入新的变形均衡性鼓励损失函数，对跨模式图像合成问题的问题提出了一个通用解决方案。该方法包括对图像合成网络的联合培训以及单独的注册网络，并允许在输入上进行对抗训练，即使使用未对准数据。这项工作通过允许对更困难的数据集进行跨模式图像合成网络的毫不费力培训来降低新的临床应用程序的标准，并为开发新的基于通用学习的跨模式注册算法开发机会。

U-shaped networks are widely used in various medical image tasks, such as segmentation, restoration and reconstruction, but most of them usually rely on centralized learning and thus ignore privacy issues. To address the privacy concerns, federated learning (FL) and split learning (SL) have attracted increasing attention. However, it is hard for both FL and SL to balance the local computational cost, model privacy and parallel training simultaneously. To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL. Previous works cannot preserve the data privacy, including the input, model parameters, label and output simultaneously. To effectively deal with all of them, we design a novel splitting method for U-shaped medical image networks, which splits the network into three parts hosted by different parties. Besides, the distributed learning methods usually suffer from a drift between local and global models caused by data heterogeneity. Based on this consideration, we propose a dynamic weight correction strategy (\textbf{DWCS}) to stabilize the training process and avoid model drift. Specifically, a weight correction loss is designed to quantify the drift between the models from two adjacent communication rounds. By minimizing this loss, a correction model is obtained. Then we treat the weighted sum of correction model and final round models as the result. The effectiveness of the proposed RoS-FL is supported by extensive experimental results on different tasks. Related codes will be released at https://github.com/Zi-YuanYang/RoS-FL.

\ textit {objection：}基于gadolinium的对比剂（GBCA）已被广泛用于更好地可视化脑磁共振成像中的疾病（MRI）。然而，大脑和身体内部的gadolin量引起了人们对使用GBCA的安全问题。因此，在提供类似的对比度信息的同时，可以减少甚至消除GBCA暴露的新方法的发展将在临床上具有重大用途。 \ textit {方法：}在这项工作中，我们提出了一种基于深度学习的方法，用于对脑肿瘤患者的对比增强T1合成。 3D高分辨率完全卷积网络（FCN）通过处理和聚合并行的多尺度信息保持高分辨率信息，旨在将前对比度MRI序列映射到对比度增强的MRI序列。具体而言，将三个前对比的MRI序列T1，T2和表观扩散系数图（ADC）用作输入，而对比后T1序列则被用作目标输出。为了减轻正常组织与肿瘤区域之间的数据不平衡问题，我们引入了局部损失，以改善肿瘤区域的贡献，从而可以更好地增强对肿瘤的增强结果。 \ textIt {结果：}进行了广泛的定量和视觉评估，我们提出的模型在大脑中达到28.24db的PSNR，在肿瘤区域达到21.2db。 \ textit {结论和意义：}我们的结果表明，用深度学习产生的合成对比图像代替GBCA的潜力。代码可在\ url {https://github.com/chenchao666/contrast-enhanced-mri-synthesis中获得

数据隐私已成为机器学习（ML）日益重要的问题，其中许多方法已经发展以解决这一挑战，例如，这一挑战加密（同性恋加密（HE），差异隐私（DP）等）和协作培训（安全多方计算（MPC），分布式学习和联合学习（FL））。这些技术特别侧重于数据加密或安全本地计算。他们将中间信息转移到第三方以计算最终结果。梯度交换通常被认为是在深度学习（DL）中协同训练鲁棒模型的安全方式。然而，最近的研究表明，可以从共享梯度恢复敏感信息。特别地，生成的对抗网络（GaN）已显示有效地恢复这些信息。然而，基于GaN的技术需要附加信息，例如类标签，这些标签通常不可用才能获得隐私保留的学习。在本文中，我们表明，在FL系统中，仅通过我们所提出的生成回归神经网络（GRNN）只能通过共享梯度全额从共享梯度容易地恢复基于图像的隐私数据。我们制定攻击是回归问题，并通过最小化梯度之间的距离来优化生成模型的两个分支。我们在几种图像分类任务上评估我们的方法。结果说明我们所提出的GNN优于最先进的方法，具有更好的稳定性，更强的鲁棒性和更高的准确性。它对全球流动模型也没有收敛要求。此外，我们使用面部重新识别来展示信息泄漏。在这项工作中还讨论了一些防御策略。

图表卷积神经网络（GCNS）广泛用于图形分析。具体地，在医学应用中，GCNS可用于群体图中的疾病预测，其中曲线图节点代表个体，边缘代表个体相似度。然而，GCNS依赖于大量数据，这是对单一医学机构收集的具有挑战性。此外，大多数医疗机构继续面临的危急挑战是用不完全的数据信息分离地解决疾病预测。为了解决这些问题，联合学习（FL）允许隔离本地机构协作，没有数据共享的全局模型。在这项工作中，我们提出了一个框架FEDNI，通过FL释放网络染色和机构间数据。具体地，我们首先使用图形生成的对冲网络（GaN）联接捕获缺少节点和边缘预测器来完成本地网络的缺失信息。然后我们使用联合图形学习平台跨过机构训练全局GCN节点分类器。新颖的设计使我们能够通过利用联合学习和图表学习方法来构建更准确的机器学习模型。我们证明，我们的联邦模式优于本地和基线流动方法，在两个公共神经影像数据集中具有显着的边缘。

基于学习的MRI翻译涉及一个合成模型，该模型将源对比度映射到目标对比图像上。多机构合作是跨广泛数据集培训合​​成模型的关键，但是集中式培训涉及隐私风险。联合学习（FL）是一个协作框架，相反，采用分散培训，以避免共享成像数据并减轻隐私问题。但是，成像数据的分布中固有的异质性可能会损害训练的模型。一方面，即使对于具有固定源目标配置的常见翻译任务，图像分布的隐式变化也很明显。相反，当规定具有不同源目标配置的不同翻译任务时，在站点内和跨站点内会出现明确的变化。为了提高针对域转移的可靠性，我们在这里介绍了MRI合成的第一种个性化FL方法（PFLSYNTH）。 PFLSYNTH基于配备映射器的对抗模型，该映射器会产生特定于单个站点和源目标对比的潜伏期。它利用新颖的个性化阻滞了基于这些潜伏期的发电机跨发电机图的统计和加权。为了进一步促进位点特异性，在发电机的下游层上采用了部分模型聚集，而上游层则保留在本地。因此，PFLSYNTH可以培训统一的合成模型，该模型可以可靠地跨越多个站点和翻译任务。在多站点数据集上进行的全面实验清楚地证明了PFLSHNTH在多对比度MRI合成中对先前联合方法的增强性能。

Federated学习（FL）最近已成为流行的隐私合作学习范式。但是，它遭受了客户之间非独立和相同分布的（非IID）数据的困扰。在本文中，我们提出了一个新颖的框架，称为合成数据辅助联合学习（SDA-FL），以通过共享合成数据来解决这一非IID挑战。具体而言，每个客户端都预测了本地生成对抗网络（GAN）以生成差异化私有合成数据，这些数据被上传到参数服务器（PS）以构建全局共享的合成数据集。为了为合成数据集生成自信的伪标签，我们还提出了PS执行的迭代伪标记机制。本地私人数据集和合成数据集与自信的伪标签的结合可导致客户之间的数据分布几乎相同，从而提高了本地模型之间的一致性并使全球聚合受益。广泛的实验证明，在监督和半监督的设置下，所提出的框架在几个基准数据集中的大幅度优于基线方法。

基于深度学习的计算机辅助诊断（CAD）已成为医疗行业的重要诊断技术，有效提高诊断精度。然而，脑肿瘤磁共振（MR）图像数据集的稀缺性导致深度学习算法的低性能。传统数据增强（DA）生成的转换图像的分布本质上类似于原始的图像，从而在泛化能力方面产生有限的性能。这项工作提高了具有结构相似性损失功能（PGGAN-SSIM）的GAN的逐步生长，以解决图像模糊问题和模型崩溃。我们还探讨了其他基于GAN的数据增强，以证明所提出的模型的有效性。我们的结果表明，PGGAN-SSIM成功地生成了256x256的现实脑肿瘤MR图像，填充了原始数据集未发现的真实图像分布。此外，PGGAN-SSSIM超过了其他基于GAN的方法，实现了FRECHET成立距离（FID）和多尺度结构相似性（MS-SSIM）的有希望的性能提升。

Quantifying the perceptual similarity of two images is a long-standing problem in low-level computer vision. The natural image domain commonly relies on supervised learning, e.g., a pre-trained VGG, to obtain a latent representation. However, due to domain shift, pre-trained models from the natural image domain might not apply to other image domains, such as medical imaging. Notably, in medical imaging, evaluating the perceptual similarity is exclusively performed by specialists trained extensively in diverse medical fields. Thus, medical imaging remains devoid of task-specific, objective perceptual measures. This work answers the question: Is it necessary to rely on supervised learning to obtain an effective representation that could measure perceptual similarity, or is self-supervision sufficient? To understand whether recent contrastive self-supervised representation (CSR) may come to the rescue, we start with natural images and systematically evaluate CSR as a metric across numerous contemporary architectures and tasks and compare them with existing methods. We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings. We also demonstrate that CSR can significantly improve image quality in two image synthesis tasks. Finally, our extensive results suggest that perceptuality is an emergent property of CSR, which can be adapted to many image domains without requiring annotations.