A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p<0.0001) and 10.18% (p<0.0001), respectively.     

Generalized overlap measures for evaluation and validation in medical image analysis

Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms.     

一种改进的CLIQUE高维子空间聚类算法

针对红外图像由于目标和背景边界模糊, 采用单一熵阈值法进行图像分割结果不理想, 提出了一种基于距离灰度补偿的红外图像增强方法, 利用距离作为空间信息对灰度进行补偿, 改善了目标和背景边界模糊对图像分割的不利影响; 然后提出了一种基于交叉熵约束的最大熵阈值图像分割方法, 在交叉熵约束保证类间差异的前提下利用类内均匀性进行图像分割, 避免了单一熵方法阈值的局限性。实验结果表明, 对小目标复杂背景和复杂目标大背景的红外图像, 所提出的方法得到了准确的图像分割结果。     

改进U-Net网络的接地网图像超像素分割北大核心CSCD

研究基于改进U-Net网络的接地网图像超像素分割方法,提升红外图像超像素分割效果。通过主成分分析法降维处理接地网腐蚀红外图像;利用Turbopixel超像素分割法分割降维后的红外图像,获取数个超像素区域;在全卷积U-Net网络内添加可变形卷积与重构上采样卷积,并利用反向传播算法,优化网络参数,建立改进的全卷积U-Net网络结构;在改进的全卷积U-Net网络内分割获取的数个超像素区域,输出红外图像超像素自动分割结果。实验证明:该方法可有效降维处理接地网腐蚀红外图像,实现红外图像超像素分割,分割后的红外图像边界清晰;在不同分辨率时,该方法的Dice相似性系数较高、Hausdorff距离较低,具备较高的红外图像超像素分割精度。     

Multiple abdominal organ segmentation: an atlas-based fuzzy connectedness approach.

Organ segmentation is an important step in various medical image applications. In this paper, a presegmented atlas is incorporated into the fuzzy connectedness (FC) framework for automatic segmentation of abdominal organs. First, the atlas is registered onto the subject to provide an initial segmentation. Then, a novel method is applied to estimate the necessary FC parameters such as organ intensity features, seeds, and optimal FC threshold automatically and subject adaptively. In order to overcome the intensity overlapping between the neighboring organs, a shape modification approach based on Euclidean distance and watershed segmentation is used. This atlas-based segmentation method has been tested on some abdominal CT and MRI images from Chinese patients. Experimental results indicate the validity of this segmentation method for various image modalities.     

MR Image Segmentation Using a Power Transformation Approach

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.      

一种改进的图谱阈值分割算法

针对图像分割是典型的结构不良问题,将图谱划分理论作为一种新型的模式分析工具应用到图像分割并引起广大学者关注。考虑到现有的图谱阈值法中图权计算方法采用基于欧氏距离的幂指数函数导致其计算量过大的不足,首先采用基于欧氏距离的分式型柯西函数代替基于欧氏距离的幂指数函数提出图权计算的新方法,其次将其应用基于图谱划分测度的图像阈值分割算法中并得到一种改进的图谱阈值分割方法。实验结果表明,该方法的计算量小且对目标和背景相差比例较大的图像能获得满意的结果。     

Computer-aided kidney segmentation on abdominal CT images.

In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation.     

基于多尺度空洞分离卷积的U-Net分割前列腺图像

前列腺核磁共振(magnetic resonance,MR)影像切片后发现有些影像 没有有效的边缘信息,这导致无法明 确定位边缘位置,进而无法分割出前列腺。同时,传统的卷积网络需要参数量庞大占用模型 的存储空间过多。本文提出了一种结合多尺度空洞可分离卷积和通道注意力的U-Net来分 割 前列腺的方法。首先,对50个3维(three-dimensional,3D)前列腺样品进行切片并对切片后图像 进行对比度增强。随 后,将处理后数据输入到残差U-Net中,使用多尺度空洞卷积和通道注意力作为编码-解 码 单元来提取特征信息。最后,使用Dice系数和豪斯多夫距离(Housdorff distance, HD)来评估分割结果。实验 在PROMISE12挑战赛数据集验证,最终Dice系数和HD分别为88.13%、14.17 mm,参数量和 存储空间降低57%。结果表明,本文方法不仅可以分割出没有有效边缘 的前列腺区域提高其分 割精度而且能有效的降低参数量和存储空间,能够应用于模糊边缘的医学图像中。     

Bilateral filtering of diffusion tensor magnetic resonance images.

We extend the well-known scalar image bilateral filtering technique to diffusion tensor magnetic resonance images (DTMRI). The scalar version of bilateral image filtering is extended to perform edge-preserving smoothing of DT field data. The bilateral DT filtering is performed in the Log-Euclidean framework which guarantees valid output tensors. Smoothing is achieved by weighted averaging of neighboring tensors. Analogous to bilateral filtering of scalar images, the weights are chosen to be inversely proportional to two distance measures: The geometrical Euclidean distance between the spatial locations of tensors and the dissimilarity of tensors. We describe the noniterative DT smoothing equation in closed form and show how interpolation of DT data is treated as a special case of bilateral filtering where only spatial distance is used. We evaluate different recent DT tensor dissimilarity metrics including the Log-Euclidean, the similarity-invariant Log-Euclidean, the square root of the J-divergence, and the distance scaled mutual diffusion coefficient. We present qualitative and quantitative smoothing and interpolation results and show their effect on segmentation, for both synthetic DT field data, as well as real cardiac and brain DTMRI data.     

基于FCM的分块自适应图像分割方法研究

基于模糊C均值聚类（FCM）的图像分割是应用较为广泛的图像分割方法之一,但是传统的模糊C均值聚类算法都是基于欧氏距离的,对于图像中的噪声是十分敏感的。针对这一局限性,提出一种基于FCM的分块自适应图像分割方法。该方法不仅考虑了噪声不均匀分布对分割结果的影响,而且充分考虑了图像像素的灰度信息和空间信息。通过对含有噪声的自然图像和合成图像的分割试验,我们可以得到,与传统的FCM图像分割算法相比,本文方法能显著提高含有噪声图像的分割质量。     

基于多尺度特征提取的MRI脑肿瘤图像分割研究

针对磁共振成像(magnetic resonance imaging, MRI)脑部肿瘤区域误识别及肿瘤形状 差异较大的问题,提出一种基于多尺度特征提取的 MRI 脑肿瘤图像分割方法。分割模型以 U-Net 为骨干网络,使用密集金字塔卷积(dense pyramidal convolution, DPC)提取多尺度特征, 以适应不同尺寸肿瘤的分割,同时引入条状池化(strip pooling, SP),凭借其能捕获肿瘤中远 距离区域的依赖关系,进一步加强对肿瘤图像的分割能力。提出的方法在 Kaggle_3m 数据 集上进行了实验验证,实验结果表明该方法具有良好的脑部肿瘤分割性能, 其中Dice相似 系数、杰卡德系数分别达到了91.66%,84.38% 。     

基于双解码路径DD-UNet的脑肿瘤图像分割算法

针对医学图像中病灶区域尺度不一、边界模糊和周围组织强度不均匀所导致的分割精度降低问题,提出了一种基于双解码器的脑肿瘤图像分割模型。为了增强特征的表征力,提出了高阶微分残差模块并使用不同空洞率的扩张卷积用于提取特征编码,提高了网络模型的分割性能;引入上下文语义信息感知模块(multi scale dilation, MSD),从不同的目标尺度中提取更多的精细信息,提高了对结构细节信息的捕获能力,同时减少了编解码器之间的特征差异;在空间解码路径中使用选择性聚合空间注意力模块(spatial aggregation attention module, SAAM),增加了对有效空间特征的权重比例,减少了无效的特征干扰。在脑肿瘤数据集上进行了实验验证,实验结果表明,所提算法的Dice系数、平均交并比、敏感性、特异性、准确率等指标分别为：93.35%、90.71%、91.15%、99.94%、96.75%。     

改进的全卷积神经网络的脑肿瘤图像分割

针对现有机器学习算法分割脑肿瘤图像精度不高的问题,提出一种基于改进的全卷积神经网络的脑肿瘤图像分割算法。算法首先将FLAIR、T2和T1C三种模态的MR脑肿瘤图像进行灰度归一化,随后利用灰度图像融合技术得到肿瘤信息更加全面的预处理图像;然后采用融合三次脑肿瘤特征信息的改进全卷积神经网络对预处理图像进行粗分割,并且在每个卷积层后加入批量正则化层以加快网络训练的收敛速度,提高训练模型精度;最后融合全连接条件随机场细化粗分割结果中的脑肿瘤边界。实验结果表明,相较于传统的卷积神经网络脑肿瘤图像分割算法,本算法在分割精度和稳定性上有了较大提升,平均Dice可达91.29%,实时性较好,利用训练模型平均1s内可完成单张脑肿瘤图像的分割。      

Improved labeling of subcortical brain structures in atlas-based segmentation of magnetic resonance images

Precise labeling of subcortical structures plays a key role in functional neurosurgical applications. Labels from an atlas image are propagated to a patient image using atlas-based segmentation. Atlas-based segmentation is highly dependent on the registration framework used to guide the atlas label propagation. This paper focuses on atlas-based segmentation of subcortical brain structures and the effect of different registration methods on the generated subcortical labels. A single-step and three two-step registration methods appearing in the literature based on affine and deformable registration algorithms in the ANTS and FSL algorithms are considered. Experiments are carried out with two atlas databases of IBSR and LPBA40. Six segmentation metrics consisting of Dice overlap, relative volume error, false positive, false negative, surface distance, and spatial extent are used for evaluation. Segmentation results are reported individually and as averages for nine subcortical brain structures. Based on two statistical tests, the results are ranked. In general, among four different registration strategies investigated in this paper, a two-step registration consisting of an initial affine registration followed by a deformable registration applied to subcortical structures provides superior segmentation outcomes. This method can be used to provide an improved labeling of the subcortical brain structures in MRIs for different applications.     

基于模糊信息原理的纹理图像分割方法

提出了一种基于同步自回归（ＳＡＲ） 模型和模糊信息原理进行纹理分割的方法。利用二阶ＳＡＲ 模型对图像纹理进行描述,用最小平方误差方法对模型参数进行估计,在对模型参数分析的基础上,将估计的参数进行改进后作为纹理的特征向量用于纹理图像的分类与分割。由于实际图像带有许多的模糊信息,组成纹理的基元和基元之间的关系也具有很大的模糊性,文中根据模糊信息原理,分析了纹理图像的模糊特性,给出了一种基于模糊贴近度的纹理分割方法。实验结果表明,与常规的距离方法相比,用文中的方法进行图像纹理分割能取得更好的效果。     

Active contour evolved by joint probability classification on Riemannian manifold

In this paper, we present an active contour model for image segmentation based on a nonparametric distribution metric without any intensity a priori of the image. A novel nonparametric distance metric, which is called joint probability classification, is established to drive the active contour avoiding the instability induced by multimodal intensity distribution. Considering an image as a Riemannian manifold with spatial and intensity information, the contour evolution is performed on the image manifold by embedding geometric image feature into the active contour model. The experimental results on medical and texture images demonstrate the advantages of the proposed method.     

Atlas renormalization for improved brain MR image segmentation across scanner platforms

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies.     

基于双支路特征融合的MRI颅脑肿瘤图像分割研究北大核心CSCD

针对磁共振成像(magnetic resonance imaging, MRI)颅脑肿瘤区域误识别与分割网络空间信息丢失问题,提出一种基于双支路特征融合的MRI脑肿瘤图像分割方法。首先通过主支路的重构VGG与注意力模型(re-parameterization visual geometry group and attention model, RVAM)提取网络的上下文信息,然后使用可变形卷积与金字塔池化模型(deformable convolution and pyramid pooling model, DCPM)在副支路获取丰富的空间信息,之后使用特征融合模块对两支路的特征信息进行融合。最后引入注意力模型,在上采样过程中加强分割目标在解码时的权重。提出的方法在Kaggle_3m数据集和BraTS2019数据集上进行了实验验证,实验结果表明该方法具有良好的脑肿瘤分割性能,其中在Kaggle_3m上,Dice相似系数、杰卡德系数分别达到了91.45%和85.19%。     

多分类CNN的胶质母细胞瘤多模态MR图像分割

为提高胶质母细胞瘤（GBM）多模态磁共振（MR）图像中各肿瘤子区域分割的准确性,提出一种多分类卷积神经网络（CNN）的GBM多模态MR图像自动分割算法.首先在98%缩尾处理和配准GBM多模态MR图像后,利用N4ITK法校正偏移场;其次构建一个主要由4个卷积层、2个池化层和2个全连接层组成的多分类CNN模型,训练后预分割GBM多模态MR图像,将体素分为5类不同的标签;最后移除所有小于200体素的假阳性区域,中值滤波后获得最终分割结果.以Dice相似性系数DSC、阳性预测值PPV和平均Hausdorff距离AHD为评价指标,利用所提出的算法对F-C-GBM数据集中整个肿瘤组织进行分割,获得的DSC、PPV、AHD分别为0.889±0.087、0.859±0.127和1.923.结果表明,该算法能有效提高GBM多模态MR图像分割的性能,可望有临床应用前景.