Volume 29, Issue 10

We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans—with manually segmented white matter, cerebral cortex, ventricles and subcortical structures—to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.

我们提出了一种非参数、概率模型，用于在给定一组训练图像和对应标签图的情况下自动分割医学图像。由此产生的推理算法依赖于测试图像与单个训练图像之间的成对配准。然后将训练标签转移到测试图像并融合，以计算测试对象的最终分割。这种标签融合方法已被证明能产生准确的分割，因为使用多个配准能够捕获更大的受试者间解剖变异性，并提高对偶发配准失败的鲁棒性。据我们所知，本文首次提出了一个全面的概率框架，严格地证明了标签融合作为一种分割方法的合理性。所提出的框架使我们能够在理论上和实践上比较不同的标签融合算法。特别是，最近的标签融合或多图谱分割算法被解释为我们框架的特例。我们进行了两组实验来验证所提出的方法。在第一组实验中，我们使用39个脑部MRI扫描（具有手动分割的白质、大脑皮层、脑室和皮层下结构）来比较不同的标签融合算法和广泛使用的FreeSurfer全脑分割工具。我们的结果表明，所提出的框架比FreeSurfer和之前的标签融合算法产生更准确的分割。在第二个实验中，我们使用282名受试者的脑部MRI扫描，证明所提出的分割工具足够敏感，能够在衰老和阿尔茨海默病的研究中稳健地检测海马体积变化。

Fast magnetic resonance imaging slice acquisition techniques such as single shot fast spin echo are routinely used in the presence of uncontrollable motion. These techniques are widely used for fetal magnetic resonance imaging (MRI) and MRI of moving subjects and organs. Although high-quality slices are frequently acquired by these techniques, inter-slice motion leads to severe motion artifacts th...

快速磁共振成像切片采集技术，如单次激发快速自旋回波，常在存在不可控运动的情况下常规使用。这些技术广泛应用于胎儿磁共振成像（MRI）以及移动对象和器官的MRI。尽管这些技术通常能获取高质量切片，但切片间运动会导致严重的运动伪影...

Doppler echocardiography remains the most extended clinical modality for the evaluation of left ventricular (LV) function. Current Doppler ultrasound methods, however, are limited to the representation of a single flow velocity component. We thus developed a novel technique to construct 2D time-resolved (2D+t) LV velocity fields from conventional transthoracic clinical acquisitions. Combining colo...

多普勒超声心动图仍然是评估左心室功能最广泛的临床方式。然而，当前的脉冲多普勒超声方法仅限于表示单个血流速度分量。因此，我们开发了一种新技术，从常规经胸临床采集构建二维时间分辨（2D+t）左心室速度场。结合颜色...

Magnetoacoustic tomography with magnetic induction (MAT-MI) is an imaging approach proposed to conduct noninvasive electrical conductivity imaging of biological tissue with high spatial resolution. In the present study, based on the analysis of the relationship between the conductivity distribution and the generated MAT-MI acoustic source, we propose a new multi-excitation MAT-MI approach and the ...

磁感应磁声断层成像（MAT-MI）是一种旨在以高空间分辨率对生物组织进行无创电导率成像的成像方法。在本研究中，基于对电导率分布与产生的MAT-MI声源之间关系的分析，我们提出了一种新的多激励MAT-MI方法，并且...

We present a novel 3-D scalable compression method for medical images with optimized volume of interest (VOI) coding. The method is presented within the framework of interactive telemedicine applications, where different remote clients may access the compressed 3-D medical imaging data stored on a central server and request the transmission of different VOIs from an initial lossy to a final lossle...

我们提出了一种新颖的具有优化感兴趣区域（VOI）编码的三维可扩展医学图像压缩方法。该方法是在交互式远程医疗应用的框架下提出的，其中不同的远程客户端可以访问存储在中央服务器上的压缩三维医学成像数据，并请求从初始有损到最终无损传输不同的VOI...

Extracting the prostate from magnetic resonance (MR) imagery is a challenging and important task for medical image analysis and surgical planning. We present in this work a unified shape-based framework to extract the prostate from MR prostate imagery. In many cases, shape-based segmentation is a two-part problem. First, one must properly align a set of training shapes such that any variation in s...

从前列腺磁共振图像中提取前列腺是一项具有挑战性且重要的任务，常用于医学图像分析和手术规划。本文提出了一种统一的基于形状的框架，用于从前列腺磁共振图像中提取前列腺。

This work is a part of our more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule measurement tasks. For such a task a low-bias size estimator is needed so that the true effect of factors such as acquisition and reconstruction parameters, nodule characteristics and others can be assessed. Towards this goal, we have developed a matched filter based...

本研究是我们更广泛探索影响肺结节测量任务准确性和精度的相互关联因素的一部分。对于此类任务，需要低偏倚尺寸估计器，以便评估采集和重建参数、结节特征等因素的真实影响。为此，我们开发了一种基于匹配滤波器的...

We have developed a novel, model-based active contour algorithm, termed “snakules”, for the annotation of spicules on mammography. At each suspect spiculated mass location that has been identified by either a radiologist or a computer-aided detection (CADe) algorithm, we deploy snakules that are converging open-ended active contours also known as snakes. The set of convergent snakules have the abi...

我们开发了一种新颖的基于模型的主动轮廓算法，称为“Snakules”，用于在乳腺X线摄影中标注棘状结构。在每个由放射科医生或计算机辅助检测（CADe）算法识别的可疑棘状肿块位置，我们部署Snakules，它们是收敛的开端主动轮廓，也称为蛇形曲线。一组收敛的Snakules具有...

Image registration is a single point of failure in the image-guided computer-assisted surgery. Registration is primarily used to align and fuse the data sets taken from patient's anatomy before and during surgeries. Point-based rigid-body registration is usually performed by identifying corresponding fiducials (either natural landmarks or implanted ones) in the data sets. Since the localization of...

图像配准是图像引导计算机辅助手术中的一个单点故障。配准主要用于在手术前和手术过程中对齐和融合来自患者解剖结构的数据集。基于点的刚体配准通常通过识别数据集中对应的基准点（自然标志或植入标志）来进行。由于基准的定位……

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