White matter lesion extension to automatic brain tissue segmentation on MRI

A fully automated brain tissue segmentation method is optimized and extended with white matter lesion segmentation. Cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) are segmented by an atlas-based k-nearest neighbor classifier on multi-modal magnetic resonance imaging data. This classifier is trained by registering brain atlases to the subject. The resulting GM segmentation is used to automatically find a white matter lesion (WML) threshold in a fluid-attenuated inversion recovery scan. False positive lesions are removed by ensuring that the lesions are within the white matter. The method was visually validated on a set of 209 subjects. No segmentation errors were found in 98% of the brain tissue segmentations and 97% of the WML segmentations. A quantitative evaluation using manual segmentations was performed on a subset of 6 subjects for CSF, GM and WM segmentation and an additional 14 for the WML segmentations. The results indicated that the automatic segmentation accuracy is close to the interobserver variability of manual segmentations.     

Probabilistic segmentation of brain tissue in MR imaging

A new method has been developed for probabilistic segmentation of five different types of brain structures: white matter, gray matter, cerebro-spinal fluid without ventricles, ventricles and white matter lesion in cranial MR imaging. The algorithm is based on information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It uses the K-Nearest Neighbor classification technique that builds a feature space from spatial information and voxel intensities. The technique generates for each tissue type an image representing the probability per voxel being part of it. By application of thresholds on these probability maps, binary segmentations can be obtained. A similarity index (SI) and a probabilistic SI (PSI) were calculated for quantitative evaluation of the results. The influence of each image type on the performance was investigated by alternately leaving out one of the five scan types. This procedure showed that the incorporation of the T1-w, PD or T2-w did not significantly improve the segmentation results. Further investigation indicated that the combination of IR and FLAIR was optimal for segmentation of the five brain tissue types. Evaluation with respect to the gold standard showed that the SI-values for all tissues exceeded 0.8 and all PSI-values exceeded 0.7, implying an excellent agreement.     

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.     

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.     

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.     

A fully automatic and robust brain MRI tissue classification method

A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.     

Learning to segment fetal brain tissue from noisy annotations

Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19–39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.     

MRS对脑星形细胞瘤周围组织的应用价值

目的应用3.0 T多体素氢质子磁共振波谱（1H-MRS）探讨不同级别脑星形细胞瘤周围组织代谢物的表现,评价MRS定量比值在星形细胞瘤周围组织的应用价值。方法82例经病理组织学证实脑星形细胞瘤1H-MRS影像学资料,分为低级别组（WHO Ⅰ～Ⅱ）32例和高级别组（WHO Ⅲ～Ⅳ级）50例,分别测量瘤体实质区、周围组织区及对侧正常区代谢物的半定量和相对定量比值,以界值取P＜0.05。结果1H-MRS显示高级别星形细胞瘤与低级别星形细胞瘤周围组织的Cho/Cr和Cho/NAA相对定量比值差异有统计学意义（P值均＜0.01）,NAA/Cr值差异无统计意义（P＞0.05）。结论脑星形细胞瘤周围组织代谢物MRS定量比值变化反映了不同级别肿瘤生物行为,对确定脑星形细胞瘤的分级及显示侵袭的范围有重要临床实用价值。     

Individualized statistical learning from medical image databases: Application to identification of brain lesions

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a “target-specific” feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject’s images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an “estimability” criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated.     

Putting our heads together: a consensus approach to brain/non-brain segmentation in T1-weighted MR volumes

We describe an approach to brain extraction from T1-weighted MR volumes that uses a hierarchy of masks created by different models to form a consensus mask. The algorithm (McStrip) incorporates atlas-based extraction via nonlinear warping, intensity-threshold masking with connectivity constraints, and edge-based masking with morphological operations. Volume and boundary metrics were computed to evaluate the reproducibility and accuracy of McStrip against manual brain extraction on 38 scans from normal and ataxic subjects. McStrip masks were reproducible across six repeat scans of a normal subject and were significantly more accurate than the masks produced by any of the individual algorithmic components.     

自体血液回收联合控制性降压减少输血及对脑组织氧合和乳酸代谢的影响

目的观察自体血液回收和控制性降压两种减少异体输血的方法及对脊柱外科手术患者脑组织氧合和乳酸代谢的影响,探讨两种方法联合应用的安全性及有效性。方法选择2011年12月至2013年5月我院ASA分级Ⅰ^Ⅱ级脊柱外科手术患者60例,随机分成试验组(自体血回输联合控制性降压)和对照组,每组30例。两组患者均选择全身麻醉。于术前(T0)、术毕(T2)和术后24 h(T3)分别取右侧颈静脉球血和桡动脉血进行血气分析,记录动脉血氧分压(PaO2)、动脉血氧饱和度(SaO2)、颈静脉球血氧分压(PjvO2)、颈静脉球血氧饱和度(SjvO2)、动静脉血乳酸含量(LacA、Lacjv),并根据Fick公式分别计算脑动脉血氧含量(CaO2)、颈静脉球血氧含量(CjvO2)、脑氧耗[C(a-jv)O2]、脑氧摄取率(CERO2)及动静脉血乳酸含量差(ADVL)。T1为术中控制性降压稳定时的时点。记录两组患者的输液量、失血量、自体血回收量和异体血输入量及上述各时间点的血红蛋白值。结果两组患者年龄、体重、心率(HR)、术前血红蛋白水平(Hb)及术前凝血指标差异均无统计学意义(P>0.05);试验组异体血输注量明显少于对照组(P<0.01);试验组术毕(T2)、术后24 h(T3)Hb明显高于对照组(P<0.01)。两组T0时CaO2、CjvO2、CERO2和ADVL差异均无统计学意义(P>0.05)。与T0时相比,两组患者术毕(T2)及术后24 h(T3)时,CaO2、CjvO2显著下降(P<0.05或P<0.01);相同时点比较,对照组T2、T3时CaO2、CjvO2下降快于试验组,差异有统计学意义(P<0.01)。试验组T2、T3时与T0时相比,CERO2下降,差异有统计学意义(P<0.01);而对照组T2、T3时与T0时相比,CERO2上升,差异统计学意义(P<0.01)。ADVL两组各时点差异均无统计学意义(P>0.05)。试验组术中应用硝酸甘油控制性降压,术中血压(64.0±5.4)mmHg明显低于对照组(80.7±4.5)mmHg(P<0.01)。结论脊柱外科手术中自体血回收联合控制性降压能明显减少异体血输注,且能改善脑组织氧合,对乳酸代谢无影响。     

3D U-Net深度学习模型基于盆腔T2WI自动分割盆腔软组织结构

目的 评估3D U-Net深度学习(DL)模型基于盆腔T2WI自动分割盆腔软组织结构的可行性.方法 回顾性分析147例经病理证实或盆腔MRI随访观察确诊的前列腺癌或良性前列腺增生患者,其中28例接受2次、121例接受1次盆腔MR扫描,共175组T2WI;手动标注T2WI所示软组织结构,包括前列腺、膀胱、直肠、双侧精囊腺...     

Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET

Magnetic resonance imaging (MRI)-guided partial volume effect correction (PVC) in brain positron emission tomography (PET) is now a well-established approach to compensate the large bias in the estimate of regional radioactivity concentration, especially for small structures. The accuracy of the algorithms developed so far is, however, largely dependent on the performance of segmentation methods partitioning MRI brain data into its main classes, namely gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). A comparative evaluation of three brain MRI segmentation algorithms using simulated and clinical brain MR data was performed, and subsequently their impact on PVC in 18F-FDG and 18F-DOPA brain PET imaging was assessed. Two algorithms, the first is bundled in the Statistical Parametric Mapping (SPM2) package while the other is the Expectation Maximization Segmentation (EMS) algorithm, incorporate a priori probability images derived from MR images of a large number of subjects. The third, here referred to as the HBSA algorithm, is a histogram-based segmentation algorithm incorporating an Expectation Maximization approach to model a four-Gaussian mixture for both global and local histograms. Simulated under different combinations of noise and intensity non-uniformity, MR brain phantoms with known true volumes for the different brain classes were generated. The algorithms' performance was checked by calculating the kappa index assessing similarities with the "ground truth" as well as multiclass type I and type II errors including misclassification rates. The impact of image segmentation algorithms on PVC was then quantified using clinical data. The segmented tissues of patients' brain MRI were given as input to the region of interest (RoI)-based geometric transfer matrix (GTM) PVC algorithm, and quantitative comparisons were made. The results of digital MRI phantom studies suggest that the use of HBSA produces the best performance for WM classification. For GM classification, it is suggested to use the EMS. Segmentation performed on clinical MRI data show quite substantial differences, especially when lesions are present. For the particular case of PVC, SPM2 and EMS algorithms show very similar results and may be used interchangeably. The use of HBSA is not recommended for PVC. The partial volume corrected activities in some regions of the brain show quite large relative differences when performing paired analysis on 2 algorithms, implying a careful choice of the segmentation algorithm for GTM-based PVC.     

Charisma: An integrated approach to automatic H&E-stained skeletal muscle cell segmentation using supervised learning and novel robust clump splitting

Histological image analysis plays a key role in understanding the effects of disease and treatment responses at the cellular level. However, evaluating histology images by hand is time-consuming and subjective. While semi-automatic and automatic approaches for image segmentation give acceptable results in some branches of histological image analysis, until now this has not been the case when applied to skeletal muscle histology images. We introduce Charisma, a new top-down cell segmentation framework for histology images which combines image processing techniques, a supervised trained classifier and a novel robust clump splitting algorithm. We evaluate our framework on real-world data from intensive care unit patients. Considering both segmentation and cell property distributions, the results obtained by our method correspond well to the ground truth, outperforming other examined methods.     

声触诊组织量化技术评价新生大鼠缺氧缺血性脑损伤

目的探讨声触诊组织量化(VTQ)技术评价新生大鼠缺氧缺血性脑损伤(HIBD)程度的可行性。方法 7日龄Wistar新生大鼠30只,麻醉后分离右侧颈总动脉,随机分为3组。单纯缺血组10只,结扎颈总动脉;窒息组10只,结扎颈总动脉,术后恢复1h置于缺氧箱中,持续缺氧30min;对照组10只,分离颈总动脉后未予结扎。应用VTQ技术分别测量术前和术后12、24、48、72h各组大鼠的脑组织VTQ值。实验结束后处死大鼠,取出脑组织行病理检查。结果随着缺血时间延长,单纯缺血组和窒息组大鼠的VTQ值逐渐增高。单纯缺血组VTQ值在术后72h明显高于术前及对照组。窒息组VTQ值在术后24h明显增高,从术前的(0.65±0.04)m/s上升至术后72h的(0.76±0.07)m/s。病理检查可见窒息组右侧大脑皮层、海马等区域神经细胞减少,尤以海马区域更为明显;胶质细胞反应性增生,脑间质及血管周围水肿明显,室管膜区及脑室周围的脑实质内可见红细胞。结论随着缺氧缺血时间延长,大鼠脑损伤加重,其脑组织的VTQ值逐渐增高。VTQ技术可用于评价新生大鼠HIBD程度。     

脑胶质母细胞瘤与单发性脑转移瘤在常规MRI增强的鉴别诊断研究

目的探讨脑胶质母细胞瘤与单发性脑转移瘤在MR增强前后的不同影像表现,以提高对胶质母细胞瘤与单发性脑转移瘤的鉴别诊断水平。方法回顾分析经手术病理证实的脑胶质母细胞瘤26例与单发性脑转移瘤35例在T2WI/FLAIR常规MRI增强即T1WI增强的不同MR表现。结果我们发现两个较为重要征象:①假缩小征,脑胶质母细胞瘤在T1WI增强后强化的边界比在液体衰减反转恢复(FLAIR)序列T2WI上显示的边界小时我们称之为假缩小征,而单发性脑转移瘤则无此MR表现。②瘤外浸润征,脑胶质母细胞瘤呈环形强化时,环外常可见到斑片状异常强化信号,而单发性脑转移瘤强化环外则无异常强化信号。这种强化环外尚有斑片状异常强化信号,我们称之为瘤外浸润征。结论T2WI/FLAIR与T1WI增强前后对照假缩小征及瘤外浸润征可作为脑胶质母细胞瘤与单发脑转移瘤鉴别诊断的依据。     

一种基于形态学方法优化的种子填充算法及其在脑组织CT影像自动分割中的应用

医学图像的自动组织分割技术已经成为一种迫切的需求和发展趋势。本文将图像腐蚀和膨胀应用到基本种子填充算法中，提出了一种优化的种子填充分割算法，并应用于脑组织CT影像自动分割中。在进行种子选取时，利用腐蚀方法去除种子噪声，使种子的选取达到最优化。对分割后的图像借助膨胀方法消除空白点，使图像更平滑、清晰。实验证明了该方法的有效性。     

实验性大鼠脑出血急性期血浆及脑组织中白介素8表达的研究

目的 探讨白介素8(IL-8)在实验性大鼠脑出血急性期脑组织和血浆中的含量变化,阐明其在脑出血过程中的作用.方法 SD大鼠100只,体质量250～300 g,随机分为4组:对照组、小量出血组、中量出血组、大量出血组;每组又分成15个小组,每小组5只.采用未抗凝新鲜自体股动脉血注入大鼠尾状核建立脑出血动物模型.用放射免疫法测定脑出血后3 h、6 h、12 h、24 h和48 h各时间点血浆及脑组织中IL-8的含量.结果 脑组织中IL-8含量,脑出血后3 h开始升高,6 h明显增加,12 h达高峰,24 h开始下降,48 h恢复正常.6 h、12 h、24 h各实验组与对照组比较差异具有显著性(P＜0.05),6 h、12 h、24 h各组间比较大量组与中、小量组差异具有显著性(P＜0.05),中量与小量组间仅在12 h和24 h差异具有显著性(P＜0.05).血浆中IL-8含量在脑出血后3 h开始升高,6 h达高峰,12 h开始下降,24 h恢复正常.3 h、6 h、12 h各实验组与对照组比较差异具有显著性(P＜0.05),6 h、12 h各组间比较大量组与中、小量组以及中量与小量组间差异具有显著性(P＜0.05).结论 IL-8升高是脑出血后中枢神经系统受损后免疫应答致炎症反应所致,监测IL-8浓度变化对了解脑出血后的脑组织变化和判别预后有重要意义.     

SPIO标记脂肪干细胞移植治疗大鼠脑梗死的磁共振示踪成像研究

目的通过采用多聚左旋赖氨酸（PLL）与超顺磁氧化铁纳米微粒（SPIO）的复合物对脂肪干细胞（ADSCs）体外进行标记,观察SPIO标记ADSCs脑内移植治疗大鼠脑梗死的分布和迁徙情况。方法显微镜下直接结扎大脑中动脉方法制备大鼠脑梗死模型36只,随机分成缺血未干预对照组（12只）、磁性标记ADSCs移植组（12只）和未磁性标记ADSCs移植组（12只）,采用立体定向方法脑内移植。对移植后大鼠的神经系统行为和运动功能进行评估,病理组织化学染色观察ADSCs在体内的分布情况,并用磁共振成像的方法在体观察ADSCs的分布,并与病理结果进行对比。结果移植后3周神经系统行为学评分显示移植组动物明显改善,ADSCs脑内移植后3周MRT2WI显示移植区低信号改变并通过胼胝体向病灶迁移。病理组织检查显示磁共振低信号改变区可见普鲁士蓝染色阳性细胞。结论移植ADSCs可以有效地促进大鼠脑梗死后神经行为功能的恢复,MRI可用于在体评价经SPIO和PLL复合物标记的ADSCs细胞移植后在体内的分布、迁移过程。     

In-Cell Western assay: a new approach to visualize tissue factor in human monocytes

BACKGROUND: Tissue factor (TF) is an integral membrane protein essential for the initiation of the extrinsic pathway of hemostasis. A precise understanding of the TF regulation is still limited and dependent on the availability of methodological tools. Here, we describe a new approach for assessing TF amounts in human mononuclear cells (MNCs) by using the whole blood experimental conditions. AIM: In order to study TF antigen levels in human MNCs, we applied a quantitative immunostaining technique-- in-cell Western (ICW) assay using an Odyssey Infrared Imaging System. METHODS AND RESULTS: The ICW assay of TF in resting or lipopolysaccharide (LPS)-stimulated human MNCs was performed. Several sample preparation conditions were tested, namely the plating of MNCs prior to immunostaining, paraformaldehyde fixation, and an adequate cell number was used in the assay. By the use of recombinant human TF standards, it was possible, for the first time, to measure TF amounts in LPS-stimulated MNCs as 0.09 +/- 0.02 ng and 0.43 +/- 0.15 ng 10(-6) cells of surface and total TF, respectively. The concentrations of TF in resting MNCs, however, were below the detection limit. CONCLUSIONS: A novel TF ICW assay is a reproducible, time- and cost-saving method, which could become useful for studies in the fields of physiology and pathophysiology of human hemostasis.