基于3D VNetTrans的膝关节滑膜磁共振图像自动分割

膝关节是类风湿性关节炎（Rheumatoid Arthritis,RA）常见累及关节,膝关节滑膜的精准分割对RA诊断和治疗有重要影响,本文提出了一种基于VNet网络的改进算法对膝关节滑膜磁共振图像进行自动分割．首先对39名滑膜炎患者的膝关节磁共振图像进行数据预处理,通过将Transformer编码器嵌入VNet网络底部的方式构建VNetTrans网络,使用MemSwish激活函数进行训练. 最终模型平均Dice系数为0.758 5,HD为24.6 mm;相较于VNet,Dice系数提升0.083 6,HD距离减少10 mm．实验结果表明,该算法可对膝关节磁共振图像中滑膜增生区域实现较好的3D分割,具有诊断和监测RA发展过程的应用价值．     

一种用于前列腺区域分割的改进水平集算法

前列腺区域的精确分割是提高计算机辅助前列腺癌诊断准确率的重要前提.本文提出了一种新的精确的前列腺区域分割模型,分为4个步骤：首先,读取T₂加权磁共振（MR）图像;其次,利用半径为5个像素的8邻域模板（8x5）的局部二值模式（LBP）特征模板计算前列腺磁共振图像的LBP特征图;然后,利用改进的距离正则化水平集（DRLSE）模型对特征图进行分割,提取前列腺粗轮廓;最后将原始水平集能量函数进行优化,构造一个新的能量函数,提取局部灰度信息和梯度信息,并在此新的能量函数的基础上,将粗轮廓迭代演化为最终的细轮廓.本文将该模型在203组来自于国际光学与光子学学会-美国医学物理学家协会-国家癌症研究所（SPIE-AAPM-NCI）前列腺MR分类挑战数据库的T2W磁共振图像上进行了测试,并与医生手工分割结果进行了比较,结果表明本文提出模型得到的分割结果的Dice系数为0.94±0.01,相对体积差（RVD）为-1.21%±2.44%,95% Hausdorff距离（HD）为6.15±0.66 mm;与文献中现有的分割模型相比,使用本文提出的模型得到的前列腺区域分割结果更接近于手工分割的结果.     

Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms

In this article, we propose batch-type learning vector quantization (LVQ) segmentation techniques for the magnetic resonance (MR) images. Magnetic resonance imaging (MRI) segmentation is an important technique to differentiate abnormal and normal tissues in MR image data. The proposed LVQ segmentation techniques are compared with the generalized Kohonen's competitive learning (GKCL) methods, which were proposed by Lin et al. [Magn Reson Imaging 21 (2003) 863-870]. Three MRI data sets of real cases are used in this article. The first case is from a 2-year-old girl who was diagnosed with retinoblastoma in her left eye. The second case is from a 55-year-old woman who developed complete left side oculomotor palsy immediately after a motor vehicle accident. The third case is from an 84-year-old man who was diagnosed with Alzheimer disease (AD). Our comparisons are based on sensitivity of algorithm parameters, the quality of MRI segmentation with the contrast-to-noise ratio and the accuracy of the region of interest tissue. Overall, the segmentation results from batch-type LVQ algorithms present good accuracy and quality of the segmentation images, and also flexibility of algorithm parameters in all the comparison consequences. The results support that the proposed batch-type LVQ algorithms are better than the previous GKCL algorithms. Specifically, the proposed fuzzy-soft LVQ algorithm works well in segmenting AD MRI data set to accurately measure the hippocampus volume in AD MR images.     

用于压缩感知磁共振成像的分割字典学习算法

字典学习算法可以根据数据本身的特点构建稀疏域中的基,从而使数据的表示更加稀疏.该文在传统的字典学习算法基础上提出了分割字典学习算法,由于部分磁共振图像组织结构简单、可以进行图像分割,因此可根据此特点来优化字典中基函数的构建,使磁共振图像的表达更为稀疏,从而获得更高的重建图像质量.该文利用模拟数据和真实数据进行了重建实验,结果表明与传统的字典学习算法相比,分割字典学习算法能进一步改善重建图像质量.     

Automated vessel segmentation using cross-correlation and pooled covariance matrix analysis

Time-resolved contrast-enhanced magnetic resonance angiography (CE-MRA) provides contrast dynamics in the vasculature and allows vessel segmentation based on temporal correlation analysis. Here we present an automated vessel segmentation algorithm including automated generation of regions of interest (ROIs), cross-correlation and pooled sample covariance matrix analysis. The dynamic images are divided into multiple equal-sized regions. In each region, ROIs for artery, vein and background are generated using an iterative thresholding algorithm based on the contrast arrival time map and contrast enhancement map. Region-specific multi-feature cross-correlation analysis and pooled covariance matrix analysis are performed to calculate the Mahalanobis distances (MDs), which are used to automatically separate arteries from veins. This segmentation algorithm is applied to a dual-phase dynamic imaging acquisition scheme where low-resolution time-resolved images are acquired during the dynamic phase followed by high-frequency data acquisition at the steady-state phase. The segmented low-resolution arterial and venous images are then combined with the high-frequency data in k-space and inverse Fourier transformed to form the final segmented arterial and venous images. Results from volunteer and patient studies demonstrate the advantages of this automated vessel segmentation and dual phase data acquisition technique.     

A joint shape evolution approach to medical image segmentation using expectation-maximization algorithm

This study proposes an expectation–maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.     

Multicontext wavelet-based thresholding segmentation of brain tissues in magnetic resonance images

A novel segmentation method based on wavelet transform is presented for gray matter, white matter and cerebrospinal fluid in thin-sliced single-channel brain magnetic resonance (MR) scans. On the basis of the local image model, multicontext wavelet-based thresholding segmentation (MCWT) is proposed to classify 2D MR data into tissues automatically. In MCWT, the wavelet multiscale transform of local image gray histogram is done, and the gray threshold is gradually revealed from large-scale to small-scale coefficients. Image segmentation is independently performed in each local image to calculate the degree of membership of a pixel to each tissue class. Finally, a strategy is adopted to integrate the intersected outcomes from different local images. The result of the experiment indicates that MCWT outperforms other traditional segmentation methods in classifying brain MR images.     

Optimization of tissue segmentation of brain MR images based on multispectral 3D feature maps

The purpose of this work was to optimize and increase the accuracy of tissue segmentation of the brain magnetic resonance (MR) images based on multispectral 3D feature maps. We used three sets of MR images as input to the in-house developed semi-automated 3D tissue segmentation algorithm: proton density (PD) and T2-weighted fast spin echo and, T1-weighted spin echo. First, to eliminate the random noise, non-linear anisotropic diffusion type filtering was applied to all the images. Second, to reduce the nonuniformity of the images, we devised and applied a correction algorithm based on uniform phantoms. Following these steps, the qualified observer "seeded" (identified training points) the tissue of interest. To reduce the operator dependent errors, cluster optimization was also used; this clustering algorithm identifies the densest clusters pertaining to the tissues. Finally, the images were segmented using k-NN (k-Nearest Neighborhood) algorithm and a stack of color-coded segmented images were created along with the connectivity algorithm to generate the entire surface of the brain. The application of pre-processing optimization steps substantially improved the 3D tissue segmentation methodology.     

基于GPU加速的磁共振血管造影图像的并行分割与追踪算法

在应用磁共振血管造影图像进行临床诊断时,临床医生往往需要提取感兴趣区域(Region Of Interest,ROI)的部分血管.这个工作传统上需要手工进行,费时费力.该文提出一种并行的血管分割与追踪算法,利用现代图形处理器(Graphics Processing Unit,GPU)所具备的大规模并行计算能力进行快速的血管分割.首先将三维图像网格化为共面的立方体,并行处理每个立方体,确定立方体中哪些表面有血管通过,以及立方体中哪些体素包含血管.之后再将该结果用于串行的全局分割与血管追踪处理.实验结果表明,利用这种先并行后串行的方法,可以在1 s之内完成全脑血管的分割,分割的结果也更准确.     

A Texture Combined Multispectral Magnetic Resonance Imaging Segmentation for Nasopharyngeal Carcinoma

Tumor segmentation from magnetic resonance imaging (MRI) is important for volume estimation and visualization of nasopharyngeal carcinoma (NPC). In some cases, segmentation using the general multispectral (GM) method often obtained poor results due to the high false positives caused by complex anatomic structures and serious overlap in feature space. In this study, a texture combined multispectral fuzzy clustering (TCMFC) segmentation algorithm was proposed. A texture measure of T1-weighted (T1) MR image was introduced by calculating the two-order central statistical information of every pixel within a window after the window convolution operation. The texture measure and the intensities in T1 and contrast-enhanced T1 images formed the new 3-D feature vector for fuzzy clustering implemented by semi-supervised fuzzy c-means (SFCM). Testing showed that by reducing the false positives significantly, the TCMFC method achieved improved segmentation results, compared with the GM method.     

基于心脏磁共振短轴电影图像的右心室分割新进展

右心室分割对肺动脉高压、法洛四联症等疾病的心脏功能评估具有重要意义.然而，右心室结构复杂，变动性大、心肌薄且毗邻脂肪，实验全自动分割一直是难点.心脏磁共振短轴电影图像时空分辨高，常用于临床右心室分割及功能评价.本文基于心脏磁共振短轴电影图像对右心室分割方法进行了综述，首先回顾了传统右心室分割算法，然后重点介绍了基于多图谱和深度学习算法的右心室分割进展，并介绍了右心室分割结果常用的评估指标.通过上述回顾发现，基于深度学习算法的分割方法是今后临床应用的右心室分割的主要方法，对心脏相关疾病的诊断及预后十分重要，而且可大大提高医生的工作效率.     

结合小波融合和深度学习的脑胶质瘤自动分割

针对水肿区域边界模糊和瘤内结构复杂多变导致的脑胶质瘤分割不精确问题，本文提出了一种基于小波融合和3D-UNet网络的脑胶质瘤磁共振图像自动分割算法.首先，对脑胶质瘤磁共振图像的T1、T1ce、T2、Flair四种模态进行小波融合以及偏置场校正；然后，提取待分类的图像块；再利用提取的图像块训练3D-UNet网络以对图像块中的像素进行分类；最后加载损失率较小的网络模型进行分割，并采用基于连通区域的轮廓提取方法，以降低假阳性率.对57组Brats2018（Brain Tumor Segmentation 2018）磁共振图像测试集进行分割的结果显示，肿瘤的整体、核心和水肿部分的平均分割准确率（DSC）分别达到90.64%、80.74%和86.37%，这表明该算法分割脑胶质瘤准确率较高，与金标准相近.相比多模态图像融合前，该算法在减少输入网络数据量和图像冗余信息的同时，还一定程度上解决了胶质瘤边界模糊、分割不精确的问题，提高了分割的准确度和鲁棒性.     

基于COLLATE融合多图谱的心脏电影MRI右心室分割

右心室分割对肺动脉高压等疾病的心功能分析具有重要的临床意义.然而，右心室心肌薄、易变且不规则，其传统的医学图像分割方法仍然未能取得突破性进展.本文提出基于COLLATE（Consensus Level，Labeler Accuracy and Truth Estimation）的多图谱分割方法，首先以归一化互信息为相似测度对目标图像和图谱集进行B样条配准以获取粗分割结果；然后利用COLLATE对粗分割结果进行融合；最后采用基于形状约束的区域生长算法修正出现错误的数据.10例临床心脏磁共振短轴电影图像被用于算法验证.本文还将使用基于COLLATE的多图谱分割方法得到的结果与深度学习算法及手动分割进行了比较.结果显示与深度学习算法比较，使用本文算法得到的射血分数（Ejection Fraction，EF）与手动分割更加一致和相关，表明该算法的分割结果有望辅助临床心脏功能诊断.     

基于水平集的牙齿牙槽骨图像分割

口腔锥形束计算机断层扫描（Cone Beam Computed Tomography,CBCT）图像中牙齿及牙槽骨的分割对骨性结构的三维重建提供了基础,是实现牙齿牙槽骨三维可视化的必要步骤.本文根据牙齿及牙槽骨特点,将一种改进的势阱函数与水平集模型结合,克服以往势阱函数在部分区域出现“停止演化”或“过快演化”的缺陷,并将其应用在对牙齿牙槽骨的分割当中.采用多次小方差高斯滤波叠加的方式对图像进行序贯滤波预处理,解决单一方差高斯滤波难以有效滤除CBCT图像中噪声的问题,为准确分割提供了条件;基于序列图像相邻两张图片中同一牙齿的轮廓变化不大这一特点,以当前层的分割结果作为下一层曲线演化的初始轮廓,使得用更少的迭代次数得到相同结果,从而提高分割速度.另外,本文还将该算法应用于口腔磁共振图像中,并成功对单颗牙齿进行了分割.     

用于SAR估计的基于U-Net网络的快速膝关节模型重建

膝关节高场磁共振成像（MRI）时，射频功率沉积（SAR）是一个关键的安全指标.目前对于局部SAR的准确估计只能通过电磁仿真实现，这就要求得到每一个个体的膝关节模型.本文提出一种针对低场磁共振图像的基于卷积神经网络的分割方法，以实现膝关节磁共振图像的快速重建.数据集来自于矢位T₁加权自旋回波图像，将膝关节组织按照"肌肉-脂肪-骨骼"模型进行简化，除脂肪与骨骼之外的其他组织归类为肌肉.采用一种全卷积的神经网络，即U-Net进行逐层的图像分割，卷积层数为4，训练采用交叉熵函数.本文对图像的自动分割结果与手动标注结果进行了定量的比较.此外，采用3 T正交鸟笼线圈进行了SAR仿真，结果验证了组织简化对于SAR估计的可行性，并且所提方法构建的模型可以得到较为精准的局部SAR分布.     

Hepatic vessel segmentation using variational level set combined with non-local robust statistics

Hepatic vessel segmentation is a challenging step in therapy guided by magnetic resonance imaging (MRI). This paper presents an improved variational level set method, which uses non-local robust statistics to suppress the influence of noise in MR images. The non-local robust statistics, which represent vascular features, are learned adaptively from seeds provided by users. K-means clustering in neighborhoods of seeds is utilized to exclude inappropriate seeds, which are obviously corrupted by noise. The neighborhoods of appropriate seeds are placed in an array to calculate the non-local robust statistics, and the variational level set formulation can be constructed. Bias correction is utilized in the level set formulation to reduce the influence of intensity inhomogeneity of MRI. Experiments were conducted over real MR images, and showed that the proposed method performed better on small hepatic vessel segmentation compared with other segmentation methods.     

Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming

Segmentation of the left ventricle from cardiac magnetic resonance images (MRI) is very important to quantitatively analyze global and regional cardiac function. The aim of this study is to develop a novel and robust algorithm which can improve the accuracy of automatic left ventricle segmentation on short-axis cardiac MRI. The database used in this study consists of three data sets obtained from the Sunnybrook Health Sciences Centre. Each data set contains 15 cases (4 ischemic heart failures, 4 non-ischemic heart failures, 4 left ventricle (LV) hypertrophies and 3 normal cases). Three key techniques are developed in this segmentation algorithm: (1) ray scanning approach is designed for segmentation of images with left ventricular outflow tract (LVOT), (2) a region restricted technique is employed for epicardial contour extraction, and (3) an edge map with non-maxima gradient suppression approach is put forward to improve the dynamic programming to derive the epicardial boundary. The validation experiments were performed on a pool of data sets of 45 cases. For both endo- and epi-cardial contours of our results, percentage of good contours is about 91%, the average perpendicular distance is about 2 mm. The overlapping dice metric is about 0.92. The regression and determination coefficient between the experts and our proposed method on the ejection fraction (EF) is 1.01 and 0.9375, respectively; they are 0.9 and 0.8245 for LV mass. The proposed segmentation method shows the better performance and is very promising in improving the accuracy of computer-aided diagnosis systems in cardiovascular diseases.     

A fast T1 algorithm.

Multispectral tissue classification using magnetic resonance T1, T2, and rho images may be useful in diagnosing and locating certain pathology. Techniques for generating the T1 images necessary for this classification scheme often require longer data collection and post processing times than are practical. As a consequence, further development of this classification scheme may be limited. This paper addresses an improvement in the post processing time required to generate T1 images. A nonlinear least-squares algorithm is described for rapidly generating spin-lattice relaxation time images from variable repetition time magnetic resonance images. The algorithm generates a 256 x 256 pixel T1 image from nine variable repetition time images in approximately 60 sec on a VAX-6510 computer.     

Automatic segmentation of white matter lesions on magnetic resonance images of the brain by using an outlier detection strategy

White matter lesions (WMLs) are commonly observed on the magnetic resonance (MR) images of normal elderly in association with vascular risk factors, such as hypertension or stroke. An accurate WML detection provides significant information for disease tracking, therapy evaluation, and normal aging research. In this article, we present an unsupervised WML segmentation method that uses Gaussian mixture model to describe the intensity distribution of the normal brain tissues and detects the WMLs as outliers to the normal brain tissue model based on extreme value theory. The detection of WMLs is performed by comparing the probability distribution function of a one-sided normal distribution and a Gumbel distribution, which is a specific extreme value distribution. The performance of the automatic segmentation is validated on synthetic and clinical MR images with regard to different imaging sequences and lesion loads. Results indicate that the segmentation method has a favorable accuracy competitive with other state-of-the-art WML segmentation methods.