A prior feature SVM-MRF based method for mouse brain segmentation

We introduce an automated method, called prior feature Support Vector Machine-Markov Random Field (pSVMRF), to segment three-dimensional mouse brain Magnetic Resonance Microscopy (MRM) images. Our earlier work, extended MRF (eMRF) integrated Support Vector Machine (SVM) and Markov Random Field (MRF) approaches, leading to improved segmentation accuracy; however, the computation of eMRF is very expensive, which may limit its performance on segmentation and robustness. In this study pSVMRF reduces training and testing time for SVM, while boosting segmentation performance. Unlike the eMRF approach, where MR intensity information and location priors are linearly combined, pSVMRF combines this information in a nonlinear fashion, and enhances the discriminative ability of the algorithm. We validate the proposed method using MR imaging of unstained and actively stained mouse brain specimens, and compare segmentation accuracy with two existing methods: eMRF and MRF. C57BL/6 mice are used for training and testing, using cross validation. For formalin fixed C57BL/6 specimens, pSVMRF outperforms both eMRF and MRF. The segmentation accuracy for C57BL/6 brains, stained or not, was similar for larger structures like hippocampus and caudate putamen, (~ 87%), but increased substantially for smaller regions like susbtantia nigra (from 78.36% to 91.55%), and anterior commissure (from ~ 50% to ~ 80%). To test segmentation robustness against increased anatomical variability we add two strains, BXD29 and a transgenic mouse model of Alzheimer's disease. Segmentation accuracy for new strains is 80% for hippocampus, and caudate putamen, indicating that pSVMRF is a promising approach for phenotyping mouse models of human brain disorders.     

一种数字人脑部切片图像分割新方法

目的 提出一种人脑切片图像自动分割算法,以克服现有的方法对大量人工参与的依赖.方法 针对人脑切片图像的特征,提出一种基于区域生长的灰度直方图阈值化分割算法.首先通过区域生长过程对图像进行初始的粗分割,再用直方图阈值化方法进行二次细分割提取目标区域.结果 采用此方法准确有效地分割出了大脑白质和大脑皮质.结论 此算法结合切片图像的全局信息和局部信息应用于分割,是一种比较好的分割方法.     

Semiautomatic segmentation of brain exterior in magnetic resonance images driven by empirical procedures and anatomical knowledge

This work demonstrates encouraging results for increasing the automation of a practical and precise magnetic resonance brain image segmentation method. The intensity threshold for segmenting the brain exterior is determined automatically by locating the choroid plexus. This is done by finding peaks in a series of histograms taken over regions specified using anatomical knowledge. Intensity inhomogeneities are accounted for by adjusting the global intensity to match the white matter peak intensity in local regions. Automated results are incorporated into the established manually guided segmentation method by providing a trained expert with the automated threshold. The results from 20 different brain scans (over 1000 images) obtained under different conditions are presented to validate the method which was able to determine the appropriate threshold in approximately 80% of the data.     

Multi-atlas segmentation with augmented features for cardiac MR images

Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupé et al., 2011; Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features.     

Robust whole-brain segmentation: Application to traumatic brain injury

We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called “Multi-Atlas Label Propagation with Expectation–Maximisation based refinement” (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensity-refined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge. In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques. To demonstrate the versatility of the proposed approach, we employed MALP-EM to segment 125 MR brain images into 134 regions from subjects who had sustained traumatic brain injury (TBI). We employ a protocol to assess segmentation quality if no manual reference labels are available. Based on this protocol, three independent, blinded raters confirmed on 13 MR brain scans with pathology that MALP-EM is superior to established label fusion techniques. We visually confirm the robustness of our segmentation approach on the full cohort and investigate the potential of derived symmetry-based imaging biomarkers that correlate with and predict clinically relevant variables in TBI such as the Marshall Classification (MC) or Glasgow Outcome Score (GOS). Specifically, we show that we are able to stratify TBI patients with favourable outcomes from non-favourable outcomes with 64.7% accuracy using acute-phase MR images and 66.8% accuracy using follow-up MR images. Furthermore, we are able to differentiate subjects with the presence of a mass lesion or midline shift from those with diffuse brain injury with 76.0% accuracy. The thalamus, putamen, pallidum and hippocampus are particularly affected. Their involvement predicts TBI disease progression.     

Phantom-based performance evaluation: application to brain segmentation from magnetic resonance images

This paper presents a new technique for assessing the accuracy of segmentation algorithms, applied to the performance evaluation of brain editing and brain tissue segmentation algorithms for magnetic resonance images. We propose performance evaluation criteria derived from the use of the realistic digital brain phantom Brainweb. This 'ground truth' allows us to build distance-based discrepancy features between the edited brain or the segmented brain tissues (such as cerebro-spinal fluid, grey matter and white matter) and the phantom model, taken as a reference. Furthermore, segmentation errors can be spatially determined, and ranged in terms of their distance to the reference. The brain editing method used is the combination of two segmentation techniques. The first is based on binary mathematical morphology and a region growing approach. It represents the initialization step, the results of which are then refined with the second method, using an active contour model. The brain tissue segmentation used is based on a Markov random field model. Segmentation results are shown on the phantom for each method, and on real magnetic resonance images for the editing step; performance is evaluated by the new distance-based technique and corroborates the effective refinement of the segmentation using active contours. The criteria described here can supersede biased visual inspection in order to compare, evaluate and validate any segmentation algorithm. Moreover, provided a 'ground truth' is given, we are able to determine quantitatively to what extent a segmentation algorithm is sensitive to internal parameters, noise, artefacts or distortions.     

大鼠创伤性脑损伤早期基因差异表达的研究

目的研究创伤性脑损伤早期基因表达谱与正常脑组织基因表达谱的差异,以期阐明脑损伤后早期基因表达的改变规律,阐明脑损伤发生发展的分子机制,从而为临床治疗提供帮助,同时为法医损伤时间推断研究寻找标志物提供帮助。方法以大鼠自由落体损伤模型为对象,从损伤区脑组织和假手术对照组脑组织分别提取mRNA,经反转录成cDNA后与含有4096个随机基因的基因表达谱芯片杂交,杂交后的芯片经扫描仪扫描,并用GenePix3.0软件分析结果。结果发现有124个差异表达基因或表达序列标签（expression sequencetags,ESTs）;其中有46个基因和26个EST表达下调;28个基因和24个EST表达上调;在这些表达有差异的基因中,有涉及细胞内信号传导、神经递质释放、参与炎症的蛋白、离子通道及其受体蛋白和参与炎症反应的蛋白等被发现。结论创伤性脑损伤的发生发展涉及多个基因的改变;研究一个或少数几个基因很难解释其损伤后分子变化机制;基因芯片是研究颅脑损伤这种多基因改变、多因素作用的理想工具。     

Mapping postnatal mouse brain development with diffusion tensor microimaging

While mouse brain development has been extensively studied using histology, quantitative characterization of morphological changes is still a challenging task. This paper presents how developing brain structures can be quantitatively characterized with magnetic resonance diffusion tensor microimaging coupled with techniques of computational anatomy. High resolution diffusion tensor images of ex vivo postnatal mouse brains provide excellent contrasts to reveal the evolutions of mouse forebrain structures. Using anatomical landmarks defined on diffusion tensor images, tissue level growth patterns of mouse brains were quantified. The results demonstrate the use of these techniques to three-dimensionally and quantitatively characterize brain growth.     

Elovl4 mRNA在小鼠视网膜发育过程中的表达分布

目的本研究旨在观察Elovl4基因mRNA在发育期小鼠眼中的分布。方法在EST和HTGS信息库查寻Elovl4同源基因。用小鼠Elovl4探针在小鼠眼冰冻部位进行原位杂交。结果小鼠视网膜Elovl4在胚胎发育的第15天(E15)开始表达并持续到出生后各阶段。在出生后1～3天(P1-P3),Elovl4主要在视网膜神经节细胞中表达,第七天(P7)主要在视网膜外核层表达,最后的表达聚集在感光体内部节段上。结论在视网膜发育期中Elovl4的表达呈动态图谱变化。胚胎期以及出生后早期主要在神经节细胞中表达,逐步转换至后期主要在感光体内部节段上表达。     

Dissecting the role of Kr-h1 brain gene expression in foraging behavior in honey bees (Apis mellifera)

Expression of Krüppel homolog-1 (Kr-h1) in the honey bee brain is strongly associated with foraging behavior. We performed a series of studies to determine if Kr-h1 expression correlates with specific aspects of foraging. We found that Kr-h1 expression is unaffected by flight experience in male bees. Expression was unaffected by behavioral reversion of workers from foraging to brood care, suggesting that expression is not associated with the active performance of foraging, but rather with stable physiological changes. Kr-h1 expression is increased by cGMP treatment in workers, and the Kr-h1 promoter contains a conserved potential cGMP response element. Since cGMP treatment causes precocious foraging, our results suggest that Kr-h1 expression is associated with cGMP-mediated changes in the brain that occur early in the transition to foraging behavior.     

Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge

A number of algorithms for brain segmentation in preterm born infants have been published, but a reliable comparison of their performance is lacking. The NeoBrainS12 study (http://neobrains12.isi.uu.nl), providing three different image sets of preterm born infants, was set up to provide such a comparison. These sets are (i) axial scans acquired at 40 weeks corrected age, (ii) coronal scans acquired at 30 weeks corrected age and (iii) coronal scans acquired at 40 weeks corrected age. Each of these three sets consists of three T1- and T2-weighted MR images of the brain acquired with a 3T MRI scanner. The task was to segment cortical grey matter, non-myelinated and myelinated white matter, brainstem, basal ganglia and thalami, cerebellum, and cerebrospinal fluid in the ventricles and in the extracerebral space separately. Any team could upload the results and all segmentations were evaluated in the same way. This paper presents the results of eight participating teams. The results demonstrate that the participating methods were able to segment all tissue classes well, except myelinated white matter.     

Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains

This paper evaluates strategies for atlas selection in atlas-based segmentation of three-dimensional biomedical images. Segmentation by intensity-based nonrigid registration to atlas images is applied to confocal microscopy images acquired from the brains of 20 bees. This paper evaluates and compares four different approaches for atlas image selection: registration to an individual atlas image (IND), registration to an average-shape atlas image (AVG), registration to the most similar image from a database of individual atlas images (SIM), and registration to all images from a database of individual atlas images with subsequent multi-classifier decision fusion (MUL). The MUL strategy is a novel application of multi-classifier techniques, which are common in pattern recognition, to atlas-based segmentation. For each atlas selection strategy, the segmentation performance of the algorithm was quantified by the similarity index (SI) between the automatic segmentation result and a manually generated gold standard. The best segmentation accuracy was achieved using the MUL paradigm, which resulted in a mean similarity index value between manual and automatic segmentation of 0.86 (AVG, 0.84; SIM, 0.82; IND, 0.81). The superiority of the MUL strategy over the other three methods is statistically significant (two-sided paired t test, P < 0.001). Both the MUL and AVG strategies performed better than the best possible SIM and IND strategies with optimal a posteriori atlas selection (mean similarity index for optimal SIM, 0.83; for optimal IND, 0.81). Our findings show that atlas selection is an important issue in atlas-based segmentation and that, in particular, multi-classifier techniques can substantially increase the segmentation accuracy.     

A novel framework for segmentation of deep brain structures based on Markov dependence tree

The aim of this work is to develop a new framework for multi-object segmentation of deep brain structures (caudate nucleus, putamen and thalamus) in medical brain images. Deep brain segmentation is difficult and challenging because the structures of interest are of relatively small size and have significant shape variations. The structure boundaries may be blurry or even missing, and the surrounding background is full of irrelevant edges. To tackle these problems, we propose a template-based framework to fuse the information of edge features, region statistics and inter-structure constraints for detecting and locating all target brain structures such that initialization by hand is unnecessary. The multi-object template is organized in the form of a hierarchical Markov dependence tree (MDT), and multiple objects are efficiently matched to a target image by a top-to-down optimization strategy. The final segmentation is obtained through refinement by a B-spline based non-rigid registration between the exemplar image and the target image. Our approach needs only one example as training data. We have validated the proposed method on a publicly available T1-weighted magnetic resonance image database with expert-segmented brain structures. In the experiments, the proposed approach has obtained encouraging results with 0.80 Dice score for the caudate nuclei, 0.81 Dice score for the putamina and 0.84 Dice score for the thalami on average.     

Automatic morphology-based brain segmentation (MBRASE) from MRI-T1 data

A method called morphology-based brain segmentation (MBRASE) has been developed for fully automatic segmentation of the brain from T1-weighted MR image data. The starting point is a supervised segmentation technique, which has proven highly effective and accurate for quantitation and visualization purposes. The proposed method automates the required user interaction, i.e., defining a seed point and a threshold range, and is based on the simple operations thresholding, erosion, and geodesic dilation. The thresholds are detected in a region growing process and are defined by connections of the brain to other tissues. The method is first evaluated on three computer simulated datasets by comparing the automated segmentations with the original distributions. The second evaluation is done on a total of 30 patient datasets, by comparing the automated segmentations with supervised segmentations carried out by a neuroanatomy expert. The comparison between two binary segmentations is performed both quantitatively and qualitatively. The automated segmentations are found to be accurate and robust. Consequently, the proposed method can be used as a default segmentation for quantitation and visualization of the human brain from T1-weighted MR images in routine clinical procedures.     

Sex-specific, postpuberty changes in mouse brain structures revealed by three-dimensional magnetic resonance microscopy

Sexual dimorphism of brain structures has been reported in some species. We report that sex-dependent developmental structure changes exist in the C57Bl/6(J) mouse, a common model for the genetic analysis of brain function. High resolution, three-dimensional (3D) magnetic resonance microscopy (MRM) images were obtained in intact brains of male and female adult and peripubertal mice. The lateral and third ventricles, hippocampus, amygdala, striatum, and total brain were reconstructed in 3D. As observed in humans, there was overall cerebral growth from peripuberty to adulthood in both sexes. After correcting for the increased brain size, the hippocampus and amygdala were disproportionately larger in adult compared to peripubertal mice. Several sexual dimorphisms were also observed. The lateral ventricles were larger, while the amygdala (the left side in particular) was smaller in females compared to males. Lateral and third ventricles were reduced over time in males only, exhibiting a sex-specific developmental profile. The striatal size was uniform among the groups studied. The surface area of the segmented structures was assayed. Possible shape distortions were detected for the lateral ventricles, hippocampus, and overall brain structure based on a lack of covariance between the surface area and volumetric measurements. Although many sexually dimorphic changes are reported perinatally, our results suggest that there are additional sex-specific transformations that occur around puberty and persist in adulthood.     

截短型小鼠成纤维细胞生长因子受体-1基因慢病毒载体构建及在真核细胞中的表达

本研究旨在克隆小鼠成纤维细胞生长因子受体1(fibroblast growth factor receptor 1,fgfr1)基因,构建携带增强型绿色荧光蛋白(EGFP)的截短型fgfr1(△fgfr1)重组慢病毒载体并在真核细胞中表达。采用逆转录-聚合酶链反应(RT-PCR)以BALB/c胎鼠脑组织为模板克隆全长型fgfr1基因,连接至克隆载体pCR-Blunt,通过反向PCR技术删除胞内磷酸化区域获得△fgfr1,限制性内切酶酶切后亚克隆至慢病毒转移质粒,构建携带EGFP及△fgfr1双顺反子自身失活型重组慢病毒表达质粒,通过脂质体转染法与包装质粒及包膜蛋白质粒共转染包装细胞293FT,超速离心浓缩病毒颗粒后转染293FT细胞,用荧光显微镜及流式细胞术(FCM)检测EGFP的表达,免疫印迹法(Western blot)鉴定截短型FGFR1蛋白表达。结果表明,成功克隆小鼠fgfr1基因,构建重组慢病毒转移载体LV-IRES-EGFP-△fgfr1及对照载体LV-IRES-EGFP,三质粒系统共转染293FT细胞后获得病毒滴度达到108 TU/ml。以重组病毒载体转染293FT细胞后第4天在荧光显微镜下观察到较强绿色荧光表达,FCM检测转染效率可达95%,Western blot检测显示,转染后293FT细胞表达截短型FGFR1蛋白。结论:成功构建了自身失活型慢病毒载体LV-IRES-EGFP-△fgfr1,并在真核细胞中获得表达。     

Self-supervised anomaly detection,staging and segmentation for retinal images

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images. We also explore the relationship between the data transformation adopted in the SSL module and the quality of anomaly detection for retinal images. Moreover, to take full advantage of the proposed SSL-AnoVAE and apply towards clinical usages for computer-aided diagnosis of retinal-related diseases, we further propose to stage and segment the anomalies in retinal images detected by SSL-AnoVAE in an unsupervised manner. Experimental results demonstrate the effectiveness of our proposed method for unsupervised anomaly detection, staging and segmentation on both retinal optical coherence tomography images and color fundus photograph images.     

钙拮抗剂对缺血性脑损伤后Bcl-2和Bax基因表达的作用

INTRODUCTIONApoptosisisrecognizedtooccurasaresultofexecutionofexquisitelyregulatedgeneticprogram．Apoptosisisimplicatedinthepathogen－esisofanincreasingnumberofdiseasesandconsideredtobein－volvedinpathologicalcelldeathaswell．Recentevidenceindicatesasignificantcontributionofapoptosistothedelayedneuronaldeathinthehippocampusaftertransientglobalischemia[１]．Bcl－２andBaxhavebeenrecognizedtoplayanimportantroleintheregulationofcelldeathinthenervoussystem[２]．Nimodipineisaneuro－protectived…     

Microarray technology and gene expression analysis for the study of angiogenesis

Angiogenesis plays a major role in multiple disease processes including cancer, and new agents that modulate angiogenesis are rapidly entering clinical trials. The understanding of the biological mechanisms and downstream effects for many of these agents is poorly understood. It is therefore important that methods evolve to understand how an agent regulates angiogenesis, in order to promote a higher percentage of successful drug candidates. With the emergence of microarray technology for the evaluation of gene expression, researchers have a powerful tool for dissecting the biological mechanisms of angiogenesis. However, huge data sets and complex statistics pose a hurdle for the investigator to obtain useful and meaningful data. To eliminate problems in data analysis, proper design and planning prior to performing a microarray experiment is crucial to making valid conclusions. This review will discuss the critical factors in designing, performing and analysing microarray experiments, and the utility of various models of angiogenesis for microarray analysis.     

Moment-based texture segmentation of luminal contour in intravascular ultrasound images

Purpose A system for luminal contour segmentation in intravascular ultrasound images is proposed. Methods Moment-based texture features are used for clustering of the pixels in the input image. After the clustering, morphological smoothing and a boundary detection process are applied and the final image is obtained. Results The proposed method was applied to 15 images from different patients, and a correlation coefficient of 0.86 was obtained between the areas of lumen automatically and manually defined. Conclusion Moment-based texture features together with the radial feature are powerful tools for identification of the lumen region in intravascular ultrasound images. Morphological filtering was useful for improving the segmentation results.