Volume 37, Issue 3

In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.

在医学图像分析应用中，大量标注数据的可用性变得越来越关键。然而，标注的医学数据往往稀缺且获取成本高昂。在本文中，我们通过应用基于对抗学习的最新技术来解决视网膜彩色图像合成的问题。在这种设置中，生成模型被训练以最大化由第二个模型提供的损失函数，该模型试图将其输出分类为真实或合成。特别地，我们提出实现一个对抗自编码器用于视网膜血管网络合成任务。我们将生成的血管树作为生成彩色视网膜图像的中间阶段，这是通过生成对抗网络完成的。两个模型都需要优化几乎处处可微的损失函数，这使得我们可以共同训练它们。最终模型提供了一个端到端的视网膜图像合成系统，能够通过从我们强加给相关潜在空间的简单概率分布进行采样，生成用户所需的任意数量的视网膜图像及其对应的血管网络。我们表明，学习到的潜在空间包含一个定义良好的语义结构，这意味着我们可以在视网膜图像空间中进行计算，例如，在两个视网膜图像之间平滑插值新的数据点。视觉和定量结果表明，合成的图像与训练集中的图像有实质性的不同，同时在解剖结构上保持一致，并显示出合理的视觉质量。

We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modality-invariant latent space. These latent representations are then combined into a single fused repre...

我们提出了一种用于MRI合成的多输入多输出全卷积神经网络模型。该模型对缺失数据具有鲁棒性，因为它受益于额外的输入模态，但并不要求必须提供。该模型是端到端训练的，并学习将所有输入模态嵌入到一个共享的模态不变潜在空间中。然后将这些潜在表示组合成一个单一的融合表示。

It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diagnosis. Similarly, automatic histopathology image analysis algorithms rely on color or intensity measures to extract tissue features. With the increasi...

普遍认为，颜色信息对于组织病理学组织切片的自动和视觉分析至关重要。在实践中，病理学家依赖反映特定组织成分存在的颜色来建立诊断。同样，自动组织病理学图像分析算法依赖于颜色或强度测量来提取组织特征。随着日益增加...

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT's design, any number of ...

四维（4-D）扩展心脏-躯干（XCAT）系列体模旨在提供人体解剖和生理学的精确计算机模型。XCAT系列包含大量不同年龄的体模，从新生儿到成人，每个体模都包括心脏和呼吸运动的参数化模型。由于XCAT设计的高度灵活性，可以...

Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged sca...

多模态医学成像越来越多地用于复杂疾病的全面评估，无论是诊断检查还是作为医学研究试验的一部分。不同的成像模态提供关于活体组织的互补信息。然而，由于不利因素，如患者不适、成本增加、扫描时间延长等，多模态检查并不总是可行。

The purpose of this paper was to extend the extended cardiac-torso (XCAT) series of computational phantoms to include a detailed lung architecture including airways and pulmonary vasculature. Eleven XCAT phantoms of varying anatomy were used in this paper. The lung lobes and initial branches of the airways, pulmonary arteries, and veins were previously defined in each XCAT model. These models were...

本文的目的是扩展扩展的心脏躯干（XCAT）系列计算体模，以包括详细肺结构，包括气道和肺血管。本文使用了11个具有不同解剖结构的XCAT体模。每个XCAT模型中先前已定义了肺叶以及气道、肺动脉和静脉的初始分支。这些模型被...

Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis, and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database wit...

收集大量带注释的医学图像数据库对于特征提取、统计分析和机器学习算法的验证和测试至关重要。心脏电机械建模和图像合成的最新进展提供了一个基于真实网格模拟生成合成图像的框架。尽管如此，它们增强现有数据库的潜力...

The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the ...

使用合成序列是先进计算机模拟评估通过三维超声（US）和磁共振（MR）成像对心脏变形和应变进行量化的最有前景的工具之一。在本文中，我们提出了第一个能够从...生成逼真的三维合成心脏US和MR（包括电影和标记）图像序列的模拟框架。

Electrocardiographic Imaging has become an increasingly used technique for non-invasive diagnosis of cardiac arrhythmias, although the need for medical imaging technology to determine the anatomy hinders its introduction in the clinical practice. This paper explores the ability of a new metric based on the inverse reconstruction quality for the location and orientation of the atrial surface inside...

心电解剖成像已成为一种越来越多用于非侵入性诊断心律失常的技术，尽管需要医学成像技术来确定解剖结构阻碍了其在临床实践中的引入。本文探讨了一种基于逆重建质量的新指标在定位和定向心房表面内部的能力...

Predicting tumor growth and its response to therapy remains a major challenge in cancer research and strongly relies on tumor growth models. In this paper, we introduce, calibrate, and verify a novel image-driven reaction-diffusion model of avascular tumor growth. The model allows for proliferation, death and spread of tumor cells, and accounts for nutrient distribution and hypoxia. It is constrai...

预测肿瘤生长及其对治疗的反应仍然是癌症研究中的主要挑战，且高度依赖于肿瘤生长模型。在本文中，我们介绍、校准并验证了一种新型的图像驱动的无血管肿瘤生长反应扩散模型。该模型允许肿瘤细胞的增殖、死亡和扩散，并考虑了营养分布和缺氧。它受限于...

Numerical simulation of ultrasound images can facilitate the training of sonographers. An efficient and realistic model for the simulation of ultrasonic speckle is the convolution of the ultrasound point-spread function with a distribution of point scatterers. Nevertheless, for a given arbitrary tissue type, a scatterer map that would generate a realistic appearance of that tissue is not known a p...

超声图像的数值模拟有助于超声医师的培训。超声散斑模拟的一种高效且逼真的模型是将超声点扩散函数与点散射体分布进行卷积。然而，对于给定的任意组织类型，能够生成该组织真实外观的散射体映射尚不清楚。

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specification...

研究不同正电子发射断层扫描（PET）重建和运动补偿方法的性能需要准确且真实地表示在真实受试者采集过程中观察到的解剖结构和运动轨迹。由于临床协议、扫描仪规格等众多差异，生成良好控制的临床数据集是困难的。

In this paper, we aim to produce a realistic 2-D projection of the breast parenchymal distribution from a 3-D breast magnetic resonance image (MRI). To evaluate the accuracy of our simulation, we compare our results with the local breast density (i.e., density map) obtained from the complementary full-field digital mammogram. To achieve this goal, we have developed a fully automatic framework, whi...

本文旨在从三维乳腺磁共振图像生成乳腺实质分布的二维投影。为评估模拟的准确性，我们将结果与从全视野数字乳腺X线摄影获得的局部乳腺密度图进行比较。为此，我们开发了一个全自动框架……

This editorial introduces the Special Issue on Simulation and Synthesis in Medical Imaging. In this editorial, we define so-far ambiguous terms of simulation and synthesis in medical imaging. We also briefly discuss the synergistic importance of mechanistic (hypothesis-driven) and phenomenological (data-driven) models of medical image generation. Finally, we introduce the twelve papers published i...

本社论介绍了关于医学影像中模拟与合成的特刊。在这篇社论中，我们定义了迄今在医学影像中尚不明确的模拟与合成术语。我们还简要讨论了机械性（假设驱动）和现象学（数据驱动）医学图像生成模型的协同重要性。最后，我们介绍了已发表的十二篇论文...

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