Volume 38, Issue 8

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this paper, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph .

我们提出VoxelMorph，一种基于学习的快速可变形成对医学图像配准框架。传统配准方法为每对图像优化目标函数，对于大数据集或丰富形变模型可能耗时。相比之下，基于近期学习方法，我们将配准公式化为一个函数，将输入图像对映射到对齐这些图像的形变场。我们通过卷积神经网络参数化该函数，并在图像集上优化网络参数。给定一对新扫描，VoxelMorph通过直接评估函数快速计算形变场。本文探索两种不同训练策略。第一种（无监督）设置中，我们训练模型最大化基于图像强度的标准图像匹配目标函数。第二种设置中，我们利用训练数据中的辅助分割。我们证明无监督模型的准确性与最新方法相当，而速度快数个数量级。我们还显示，使用辅助数据训练的VoxelMorph在测试时提高配准精度，并评估训练集大小对配准的影响。我们的方法有望加速医学图像分析和处理流程，同时促进基于学习的配准及其应用的新方向。我们的代码在https://github.com/voxelmorph/voxelmorph免费提供。

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been propos...

由于液体积累导致的视网膜肿胀与大多数威胁视力的视网膜疾病相关。光学相干断层扫描（OCT）是评估视网膜液体存在和数量以及图像引导治疗管理的当前标准。深度学习方法已在医学影像领域产生影响，并且许多视网膜OCT分析方法已被提出……

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this paper, we propose both supervised and unsupervised machine learning stra...

借助计算机辅助诊断（CAD）工具，从放射学图像中对肿瘤进行风险分层（表征）可以更准确、更快速。通过此类工具进行肿瘤表征还可以实现非侵入性癌症分期、预后，并促进个性化治疗计划作为精准医学的一部分。在本文中，我们提出了监督和无监督机器学习策略...

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware ...

自动且准确地分类视网膜光学相干断层扫描（OCT）图像对于辅助眼科医生诊断和分级黄斑疾病至关重要。临床上，眼科医生通常根据黄斑病灶的结构来诊断黄斑疾病，其形态、大小和数量是重要的判断标准。在本文中，我们提出了一种新颖的病灶感知...

The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic f...

胰腺分割对于医学图像分析至关重要，但它面临着类别不平衡、背景干扰和非刚性几何特征等巨大挑战。为了解决这些困难，我们提出了一种基于深度Q网络（DQN）驱动的方法，结合可变形U-Net，通过显式交互上下文信息并提取各向异性特征，从而准确分割胰腺。

Glioblastoma (GBM) is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here, we provide a Bayesian machine ...

胶质母细胞瘤（GBM）是一种高度侵袭性的脑肿瘤，其细胞会浸润到当前医学扫描可见病灶轮廓之外的正常脑组织中。这些浸润细胞主要通过放疗进行治疗。现有的脑肿瘤放疗方案源于群体研究，很少考虑患者特异性情况。在此，我们提供了一种贝叶斯机器学习...

Organ localization is an essential preprocessing step for many medical image analysis tasks, such as image registration, organ segmentation, and lesion detection. In this paper, we propose an efficient method for multiple organ localization in CT image using a 3D region proposal network. Compared with other convolutional neural network-based methods that successively detect the target organs in al...

器官定位是许多医学图像分析任务（如图像配准、器官分割和病变检测）的重要预处理步骤。本文提出了一种利用3D区域提案网络在CT图像中高效定位多个器官的方法。与依次检测目标器官的其他基于卷积神经网络的方法相比，我们的方法...

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aim...

淋巴结转移是乳腺癌诊断中最重要的指标之一，传统上由病理学家在显微镜下观察。近年来，随着高通量扫描和深度学习技术的显著进步，对全切片图像进行自动组织学分析在医学图像计算领域引起了广泛关注，其目的是...

Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regress...

在超声心动图电影序列中准确检测收缩末期和舒张末期帧可能是开发用于测量心脏参数的自动系统的必要预处理步骤，但由于心脏解剖结构和心率的变化（通常与病理状态相关），该检测任务具有挑战性。我们将此问题表述为回归问题...

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classification performance of CNN models on diffusion weighted imaging (DWI) streamlines defined by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbala...

卷积神经网络（CNN）最近在生物医学成像应用中取得了巨大成功。本文研究了CNN模型在由功能性MRI（fMRI）和电刺激映射（ESM）定义的扩散加权成像（DWI）流线上的分类性能。为了从极度不平衡的数据中学习一组具有判别性和可解释性的特征，我们……

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time-consuming and sensitive to motion artifacts. A single-shot quantitative T2 mapping method based on multiple overlappin...

定量磁共振成像（MRI）对临床诊断和科学研究都具有重要价值。然而，大多数MRI实验仍然是定性的，特别是动态MRI，因为使用可变加权参数的重复采样使得定量成像耗时且易受运动伪影影响。本文提出了一种基于多次重叠回波采集与深度神经网络的单次定量T2映射方法...

Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in the presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include Tikhonov, ℓ1-norm, and total-variat...

光声层析成像涉及从测量的声学边界数据重建初始压力上升分布。在存在噪声且独立数据有限的情况下，恢复初始压力上升分布往往是一个不适定问题，需要正则化。标准的正则化方案包括Tikhonov、ℓ1范数和全变差...

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This cont...

动力学参数值，如心肌灌注，可以使用示踪动力学模型从动态对比增强磁共振成像数据中量化。然而，呼吸运动影响该过程的准确性。由于钆基对比剂通过引起的快速局部信号增强，图像序列的运动补偿很困难。

Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant component and a pie...

近期，将图像映射到结构化低秩Toeplitz或Hankel矩阵的理论已成为一种有效的图像恢复方法。在本文中，我们引入了一种广义结构化低秩算法，利用下确界卷积正则化从欠采样傅里叶系数中恢复图像。该图像被建模为一个分段常数分量和一个...

Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous ...

马尔可夫随机场（MRF）已被广泛用于将先验知识作为惩罚项或正则化项引入，以在平滑边缘包围区域的同时保持边缘锐利度，用于分段平滑图像重建。在我们早期的研究中，我们提出了一种针对低剂量CT（LdCT）扫描的MRF重建方法，利用从同一患者既往...中提取的组织特异性纹理。

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels, and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. The 2D or 3D terminations are good candidates for such seed points. In this paper, we propose an automatic method to detect termination...

从三维图像和二维图像中对树状结构（如神经元、视网膜血管和支气管）进行数字重建（追踪）对生物医学研究非常重要。许多现有的重建算法依赖于一组好的种子点。二维或三维的终止点是此类种子点的良好候选。在本文中，我们提出了一种自动检测终止点的方法...

Ultrafast acoustoelectric imaging (UAI) is a novel method for the mapping of biological current densities, which may improve the diagnosis and monitoring of cardiac activation diseases such as arrhythmias. This paper evaluates the feasibility of performing UAI in beating rat hearts. A previously described system based on a 256-channel ultrasound research platform fitted with a 5-MHz linear array w...

超快声电成像（UAI）是一种绘制生物电流密度分布的新方法，有望改善心律失常等心脏激活疾病的诊断和监测。本文评估了在跳动的大鼠心脏中进行UAI的可行性。使用基于256通道超声研究平台并配备5MHz线性阵列的系统……

Retinal template matching and registration is an important challenge in teleophthalmology with low-cost imaging devices. However, the images from such devices generally have a small field of view (FOV) and image quality degradations, making matching difficult. In this paper, we develop an efficient and accurate retinal matching technique that combines dimension reduction and mutual information (MI...

视网膜模板匹配与配准是远程眼科低成本成像设备中的一个重要挑战。然而，这类设备获取的图像通常视场较小且图像质量下降，使得匹配困难。本文提出了一种高效准确的视网膜匹配技术，结合了降维和互信息...

The overall duration of acquiring a Nyquist sampled 3D dataset can be significantly shortened by enhancing the efficiency of k-space sampling. This can be achieved by increasing the coverage of k-space for every trajectory interleave. Furthermore, acceleration is possible by making use of advantageous undersampling properties. In this paper, a versatile 3D center-out k-space trajectory based on Ja...

获取Nyquist采样3D数据集的整体持续时间可以通过提高k空间采样的效率来显著缩短。这可以通过增加每次轨迹交叉的k空间覆盖来实现。此外，利用有利的欠采样属性可以实现加速。在本文中，一种基于Ja...的通用3D中心外向k空间轨迹...

Fiber tractography based on diffusion-weighted magnetic resonance imaging is to date the only method for the three-dimensional visualization of nerve fiber bundles in the living human brain noninvasively. However, various existing methods suffer from reconstructing anatomically implausible fiber tracks due to exclusive local treatment of the input data. A method that seeks to filter out invalid tr...

基于扩散加权磁共振成像的纤维束成像，是目前唯一能够无创地在活体人脑中三维可视化神经纤维束的方法。然而，由于现有许多方法仅对输入数据进行局部处理，导致重建出解剖学上不可信的纤维轨迹。一种旨在过滤掉无效轨迹的方法......

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