Simultaneous segmentation and anatomical labeling of the cerebral vasculature

We present a novel algorithm for the simultaneous segmentation and anatomical labeling of the cerebral vasculature. Unlike existing approaches that first attempt to obtain a good segmentation and then perform labeling, we optimize for both by simultaneously taking into account the image evidence and the prior knowledge about the geometry and connectivity of the vasculature. This is achieved by first constructing an overcomplete graph capturing the vasculature, and then selecting and labeling the subset of edges that most likely represents the true vasculature. We formulate the latter problem as an Integer Program (IP), which can be solved efficiently to provable optimality. We evaluate our approach on a publicly available dataset of 50 cerebral MRA images, and demonstrate that it compares favorably against state-of-the-art methods.     

Multi-perspective label based deep learning framework for cerebral vasculature segmentation in whole-brain fluorescence images

The popularity of fluorescent labelling and mesoscopic optical imaging techniques enable the acquisition of whole mammalian brain vasculature images at capillary resolution. Segmentation of the cerebrovascular network is essential for analyzing the cerebrovascular structure and revealing the pathogenesis of brain diseases. Existing deep learning methods use a single type of annotated labels with the same pixel weight to train the neural network and segment vessels. Due to the variation in the shape, density and brightness of vessels in whole-brain fluorescence images, it is difficult for a neural network trained with a single type of label to segment all vessels accurately. To address this problem, we proposed a deep learning cerebral vasculature segmentation framework based on multi-perspective labels. First, the pixels in the central region of thick vessels and the skeleton region of vessels were extracted separately using morphological operations based on the binary annotated labels to generate two different labels. Then, we designed a three-stage 3D convolutional neural network containing three sub-networks, namely thick-vessel enhancement network, vessel skeleton enhancement network and multi-channel fusion segmentation network. The first two sub-networks were trained by the two labels generated in the previous step, respectively, and pre-segmented the vessels. The third sub-network was responsible for fusing the pre-segmented results to precisely segment the vessels. We validated our method on two mouse cerebral vascular datasets generated by different fluorescence imaging modalities. The results showed that our method outperforms the state-of-the-art methods, and the proposed method can be applied to segment the vasculature on large-scale volumes.     

基于相位信息的乳腺超声图像水平集分割

目的基于相位信息改进距离正规化水平集演化(DRLSE)模型的速度收敛项,改善对乳腺肿瘤超声图像的分割效果。方法首先,利用Log-Gabor滤波器组对图像进行频域滤波,得到一组基于相位信息的特征图。其次,在相位一致性的基础上,求出乳腺超声图像经高斯噪声补偿后的最大方向能量相位PC(M),并采用细节保留各向异性扩散滤波(DPAD)模型对PC(M)降噪,减少斑点噪声的干扰。最后,选用Sigmoid函数,将滤波后的PC(M)作为其自变量,以替换DRLSE模型中的速度收敛项。结果采用改进后的模型对多幅临床乳腺肿瘤超声图像进行分割,分割结果显示基于相位信息的正规化水平集演化(PB-DRLSE)模型在相似性(SI)、真阳性(TP)和假阴性(FN)方面均优于原始DRLSE模型(P均<0.05)。结论本研究提出的分割方法较之原始模型对乳腺肿瘤超声图像的分割更为优越。     

多层螺旋CT低剂量CTA评价大脑动脉的可行性

目的探讨64层MSCT 80 kV扫描使用低剂量对比剂的CTA评价大脑动脉的可行性。方法 2012年8月~2013年6月间住院拟做大脑动脉检查的患者62例,随机分成Ⅰ组(使用120 kV CT扫描和注射80ml碘海醇)和Ⅱ组(使用80 kV CT扫描和注射50 ml碘海醇加30 ml生理盐水)。其中I组31例(男17例,女14例,年龄37~76岁,中位年龄63岁,体重40~71 kg),Ⅱ组31例(男16例,女15例,年龄36~74岁,中位年龄59岁,体重38~73kg)。扫描后测量CTA图像中大脑中动脉M1段及基底动脉内的CT值、横轴位图像噪声及信噪比,并对大脑Willis环的VR三维图像进行3级评分;记录辐射剂量指标CTDI_(vol)和DLP。结果Ⅰ组大脑中动脉M1段和基底动脉的CT值为303.01和302.25和Ⅱ组大脑中动脉M1段和基底动脉的CT值为的307.19和306.38 HU,两组间具有显著的统计学差异(P0.05);I组图像的噪声大于Ⅱ组,但大脑Willis环的显示Ⅱ组稍优于I组。结论 64层MSCT80 kV扫描使用50 ml碘对比剂加30 ml生理盐水的CTA成像不仅能显著降低辐射剂量和碘对比剂用量同时可以保证大脑动脉的图像显示质量,对评价大脑动脉是可行的。     

Fully automatic model-based calcium segmentation and scoring in coronary CT angiography

Purpose

The paper presents new methods for automatic coronary calcium detection, segmentation and scoring in coronary CT angiography (cCTA) studies.

Methods

Calcium detection and segmentation are performed by modeling image intensity profiles of coronary arteries. The scoring algorithm is based on a simulated unenhanced calcium score (CS) CT image, constructed by virtually removing the contrast media from cCTA. The methods are implemented as part of a fully automatic system for CS assessment from cCTA.

Results

The system was tested in two independent clinical trials on 263 studies and demonstrated 0.95/0.91 correlation between the CS computed from cCTA and the standard Agatston score derived from unenhanced CS CT. The mean absolute percent difference (MAPD) of 36/39 % between the two scores lies within the error range of the standard CS CT (15–65 %).

Conclusions

High diagnostic performance, combined with the benefits of the fully automatic solution, suggests that the proposed technique can be used to eliminate the need in a separate CS CT scan as part of the cCTA examination, thus reducing the radiation exposure and simplifying the procedure.     

Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique

Purpose

An automatic, accurate and fast segmentation of hemorrhage in brain Computed Tomography (CT) images is necessary for quantification and treatment planning when assessing a large number of data sets. Though manual segmentation is accurate, it is time consuming and tedious. Semi-automatic methods need user interactions and might introduce variability in results. Our study proposes a modified distance regularized level set evolution (MDRLSE) algorithm for hemorrhage segmentation.

Methods

Study data set (from the ongoing CLEAR-IVH phase III clinical trial) is comprised of 200 sequential CT scans of 40 patients collected at 10 different hospitals using different machines/vendors. Data set contained both constant and variable slice thickness scans. Our study included pre-processing (filtering and skull removal), segmentation (MDRLSE which is a two-stage method with shrinking and expansion) with modified parameters for faster convergence and higher accuracy and post-processing (reduction in false positives and false negatives).

Results

Results are validated against the gold standard marked manually by a trained CT reader and neurologist. Data sets are grouped as small, medium and large based on the volume of blood. Statistical analysis is performed for both training and test data sets in each group. The median Dice statistical indices (DSI) for the 3 groups are 0.8971, 0.8580 and 0.9173 respectively. Pre- and post-processing enhanced the DSI by 8 and 4% respectively.

Conclusions

The MDRLSE improved the accuracy and speed for segmentation and calculation of the hemorrhage volume compared to the original DRLSE method. The method generates quantitative information, which is useful for specific decision making and reduces the time needed for the clinicians to localize and segment the hemorrhagic regions.     

Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation

Objective

A practical method for patient-specific modeling of the aortic arch and the entire carotid vasculature from computed tomography angiography (CTA) scans for morphologic analysis and for interventional procedure simulation.

Materials and methods

The method starts with the automatic watershed-based segmentation of the aorta and the construction of an a-priori intensity probability distribution function for arteries. The carotid arteries are then segmented with a graph min-cut method based on a new edge weighting function that adaptively couples voxel intensity, intensity prior, and local vesselness shape prior. Finally, the same graph-cut optimization framework is used to interactively remove a few unwanted veins segments and to fill in minor vessel discontinuities caused by intensity variations.

Results

We validate our modeling method with two experimental studies on 71 multicenter clinical CTA datasets, including carotid bifurcation lumen segmentation on 56 CTAs from the MICCAI??2009 3D Segmentation Challenge. Segmentation results show that our method is comparable to the best existing methods and was successful in modeling the entire carotid vasculature with a Dice similarity measure of 84.5% (SD?=?3.3%) and MSSD 0.48?mm (SD?=?0.12?mm.) Simulation study shows that patient-specific simulations with four patient-specific models generated by our segmentation method on the ANGIO Mentor^TM simulator platform are robust, realistic, and greatly improve the simulation.

Conclusion

This constitutes a proof-of-concept that patient-specific CTA-based modeling and simulation of carotid interventional procedures are practical in a clinical environment.     

小肠梗阻所致小肠梗死的肠系膜血管CTA表现

目的：探讨小肠梗阻所致小肠梗死的肠管及肠系膜血管CT表现。方法：回顾性分析经手术病理确诊的10例小肠梗阻导致的小肠梗死患者的CTA图像,分析内容包括肠腔、肠壁、肠系膜及其血管、腹水。结果：10例均可见不同程度的肠腔扩张,梗死肠管最大直径2.6-3.8 cm。9例见有肠腔积液。7例梗死肠壁无增厚,3例增厚,肠壁厚度0.12-0.95 cm。4例梗死肠壁无强化,6例强化减低。2例梗死肠壁内见点状气体。10例均可见不同程度的肠系膜浑浊,均有腹水存在。10例均可见系膜缘动静脉无强化,7例见肠系膜静脉增粗。5例粘连性小肠梗阻中4例见肠系膜血管移位聚集;3例小肠内疝见肠系膜血管局限性聚集并向远处呈分散状;2例小肠扭转见肠系膜血管的轴向扭曲和反折。结论：肠系膜血管形态异常、肠系膜浑浊和腹水、肠壁无增厚为小肠梗阻所致小肠梗死的主要CTA表现。     

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks

Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. More specifically, the application, steering and evaluation of motion compensation algorithms can be triggered by these measures. A Coronary Motion Forward Artifact model for CT data (CoMoFACT) is developed and applied to clinical cases with excellent image quality to introduce motion artifacts using simulated motion vector fields. The data required for supervised learning is generated by the CoMoFACT from 17 prospectively ECG-triggered clinical cases with controlled motion levels on a scale of 0–10. Convolutional neural networks achieve an accuracy of 93.3% ± 1.8% for the classification task of separating motion-free from motion-perturbed coronary cross-sectional image patches. The target motion level is predicted by a corresponding regression network with a mean absolute error of 1.12 ± 0.07. Transferability and generalization capabilities are demonstrated by motion artifact measurements on eight additional CCTA cases with real motion artifacts.     

Multi-compartment heart segmentation in CT angiography using a spatially varying gaussian classifier

Objective

A fully automated and efficient method for segmenting ten major structures within the heart in Cardiac CT Angiography data for the purposes of display or cardiac functional analysis.

Materials and methods

A spatially varying Gaussian classifier is a flexible model for segmentation, combining the advantages of atlas-based frameworks, with supervised intensity models. It is composed of an independent Gaussian classifier at each voxel and uses non-rigid registration for the initial spatial alignment. We show how this large model can be trained efficiently and present a novel smoothing technique based on normalised convolution to mitigate inherent overfitting issues. The 30 datasets used in this study are selected from a variety of different scanners in order to test the robustness and stability of the algorithm. The datasets were manually segmented by a trained clinician.

Results

The method was evaluated in a leave-one-out fashion, and the results were compared to other state of the art methods in the field, with a mean surface-to-surface distance of between 0.61 and 2.12?mm for different compartments.

Conclusion

The accuracy of this method is comparable to other state of the art methods in the field. Its benefits lie in its conceptual simplicity and its general applicability. Only one non-rigid registration is required, giving it a speed advantage over multi-atlas approaches. Further accuracy may be achievable through the incorporation of an explicit shape model.     

Three-dimensional multislice helical CT angiography of cerebral aneurysms

3DCT angiography(3D-CTA), especially multislice helical CT is a noninvasive imaging modality for cerebral aneurysms. 3D-CTA is helpful in the evaluation of the configuration of the aneurysm, the surrounding vessels, and the inside of the aneurysm dome. Clinical application of this technique in complicated large cerebral aneurysms showed that with 3DCT endoscopic imaging, anatomical details of cerebral aneurysms such as the orifice of the aneurysm, intraluminal thrombous, and calcification of the wall could be clearly demonstrated. Using the 3D-imaging method with helical CT, virtual views of various surgical approaches can be compared preoperatively. This information was found to be very useful for determining difficult aneurysms for coil embolization or direct surgery including complication and broad-based aneurysms.     

多层螺旋CT颈内动脉和脑动脉成像的临床应用

目的探讨多层螺旋CT(multi-slicespiralCTMSCT)颈内动脉和脑动脉成像(CTA)的技术和临床应用。方法21例疑为颈内动脉、脑动脉病变的患者行多层螺旋CT增强扫描,利用不同的图像后处理技术分别进行血管成像。结果21例颈内动脉和脑动脉均可清晰显示,其中正常者9例,动脉狭窄9例,动脉阻塞1例,动脉瘤1例,动静脉畸形1例。结论多层螺旋CT血管成像可清晰显示血管病变,对指导临床治疗及支架内置入有重要指导价值。     

多层CTA三维重建技术评价脑动脉瘤的临床应用:与DSA对比研究

目的探讨多层CTA三维重建(3D-MSCTA)与脑血管造影数字减影术(IADSA)诊断脑动脉瘤的临床价值.方法对37例自发性蛛网膜下腔出血患者行3D-MSCTA及IADSA检查.以手术或随访为标准评价两种检查结果及图像质量.结果发现脑动脉瘤29例34处.两种检查的敏感度、阳性预测值、阴性预测值间有极显著性差异(P<0.01),特异度、准确度间及VR/MIP、IADSA敏感度和阴性预测值间有显著性统计学差异(P<0.05).图像质量评估数值最高为VR,组间有显著性差异(P<0.01).结论对于脑动脉瘤的诊断,3D-MSCTA比DSA更敏感、更准确.     

肠系膜血管CTA诊断小肠缺血病因的应用价值

目的：探讨肠系膜血管CTA诊断小肠缺血病因的价值。方法：回顾性分析22例小肠缺血患者的肠系膜血管CTA表现，12例为急性缺血，10例为慢性缺血。分析内容包括肠系膜血管有无狭窄、扩张、充盈缺损、聚集、移位。结果：12例急性缺血病人中，肠系膜上动脉栓塞2例，CTA表现为肠系膜上动脉突然中断；肠系膜上动脉血栓形成5例，表现为管腔内充盈缺损；肠系膜上静脉及门静脉血栓3例，表现为管腔内完全性或不全性充盈缺损；肠系膜上动脉夹层2例，表现为真假腔形成及之间的内膜片。10例慢性缺血病人中，粥样硬化性缺血6例，4例CTA表现为肠系膜上动脉不同程度的狭窄，2例表现为肠系膜上动脉起始部闭塞伴Riolan血管弓形成；肠系膜上动脉起始部动脉瘤2例，表现为管腔球形扩张；系统性红斑狼疮性缺血性肠病2例，表现为肠系膜血管增粗、“梳状”排列。结论：肠系膜血管CTA能够明确诊断小肠缺血的病因。     

Adaptive local window for level set segmentation of CT and MRI liver lesions

We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p  < 0.001).     

双源CT血管减影成像在颅脑血管检查中应用及技术研究

目的:探讨双源CT减影成像技术对颅脑血管的显示能力及其临床应用价值.方法:208例患者行双源CT颅脑血管减影成像检查,采用双能量技术去除颅骨,在脑血管重建中采用根据碘含量的特殊计算方法,利用减影图像进行头部血管三维重建,分析其脑血管显示能力及对痛变的诊断价值;其中19例同时行脑数字减影血管造影检查,进行双源CT减影成像技术和数字减影血管造影对照.结果:208例双源CT减影图像均能良好显示颈内动脉、椎动脉及其分支,图像评价优秀183例(88.1%),良好19例(9.2%),差5例(2.7%).脑动脉瘤、血管畸形、动脉狭窄均能良好显示,达到诊断标准.与数字减影血管造影相比,颈内动脉1-2、5段,大脑前动脉1-5段,大脑中动脉1-4段,椎动脉、基底动脉差异无统计学意义(P>0.05);颈内动脉3-4段,大脑中动脉5段差异有统计学意义(P<0.05).结论:双源减影成像技术是一种创伤较小、快速、有效的检查技术,配合合适的后处理技术能清晰显示脑部血管细小分支及与周围组织的关系.     

CT灌注联合血管成像分析单侧大脑中动脉狭窄或闭塞患者脑血流代偿机制

目的 利用CT灌注(CTP)联合CT血管成像(CTA)分析单侧大脑中动脉(MCA)狭窄或闭塞患者的脑血管自身调节储备能力及侧支循环建立情况.方法 对30例单侧MCA狭窄或闭塞患者行CTP及CTA检查,以20名志愿者作为对照组,将对照组各参数右/左侧相对比值的95%可信区间为参考值,分析病变组各灌注参数患/健侧相对比值的特点,并利用CTA观察不同临床表现患者的侧支循环代偿情况.结果 除1例MCA狭窄患者灌注正常外,29例异常灌注分为三型:Ⅰ型, CBF与CBV正常, TTP延长(3例);Ⅱ型,CBF和CBV升高,TTP延长(16例);CBF和CBV降低,TTP延长(4例);Ⅲ型,CBF降低,CBV升高,TTP延长(6例).大面积脑梗死患者(5例)侧支循环代偿欠佳,部分合并颈动脉及椎-基底动脉狭窄;25例临床正常或表现为腔隙性梗死患者侧支循环良好.结论 CTP联合CTA可很好地评估单侧MCA狭窄或闭塞患者的脑血供和脑血流动力学,其临床转归与脑血管自身调节储备及侧支循环建立这两种脑血流代偿机制密切相关.     

基于均值漂移图像分割技术去除CT图像金属伪影

CT系统在诊断时,金属作为一种高密度的物体在射线通过时的衰减系数比人体其他组织高很多,从而引起X射线被这些物体作用后急剧衰减,导致对应的投影数据失真,重建时图像上会出现伪影.这些由于金属置入物带来的伪影被统称为金属伪影.而传统的方法不能完全准确分割,所以不满足临床要求.应用均值漂移分割图像技术分割金属图像,并应用临床数据进行了验证,结果证明均值漂移分割技术用于CT金属伪影去除分割过程效果比较理想.     

Hessian filter-assisted full diameter at half maximum (FDHM) segmentation and quantification method for optical-resolution photoacoustic microscopy

Optical-resolution photoacoustic microscopy has been validated as an ideal tool for angiographic studies. Quantitative vascular analysis reveals critical information where vessel segmentation plays the key step. The comm-only used Hessian filter method suffers from varying accuracy due to the multi-kernel strategy. In this work, we developed a Hessian filter-assisted, adaptive thresholding vessel segmentation algorithm. Its performance is validated by a digital phantom and in vivo images which demonstrates a superior and consistent accuracy of 0.987 regardless of kernel selection. Subtle vessel change detection is further tested in two longitudinal studies on blood pressure agents. In the antihypotensive case, the proposed method detected a twice larger vasoconstriction over the Hessian filter method. In the antihypertensive case, the proposed method detected a vasodilation of 21.2%, while the Hessian filter method failed in change detection. The proposed algorithm may further push the limit of quantitative imaging on angiographic applications.     

Accurate quantification of vessel cross-sectional area using CT angiography: a simulation study

Coronary computed tomography (CT) angiography is a noninvasive method for visualizing coronary atherosclerosis. However, CT angiography is limited in assessment of stenosis severity by the partial volume effect and calcification. Therefore, a quantitative method for assessment of stenosis severity is needed. Polyenergetic fan beam CT simulations were performed to match the geometry of a 320-slice CT scanner. Contrast-enhanced vessel lumens were modeled as 8 mg/ml Iodine solution against a lipid background. Normal vessels were simulated by circles with diameters in the range of 0.1–3 mm. To simulate lesions, 2, 3, and 4 mm diameter vessels were simulated with area stenoses in a range of 10–90?%. The occlusion was created by a circular region of lipid placed within the lumen resulting in a crescent shaped lumen. Each vessel was simulated three times to obtain multiple noise realizations for a total of 126 vessels. Two trained readers performed manual cross-sectional area measurements in simulated normal and stenotic vessels. A new, semi-automated technique based on integrated Hounsfield units was also used to calculate vessel cross-sectional area. There was an excellent correlation between the measured and the known cross-sectional area for both normal and stenotic vessels using the manual and the semi-automated techniques. However, the overall measurement error for the manual method was more than twice as compared with the integrated HU technique. Determination of vessel cross-sectional area using the semi-automated integrated Hounsfield unit technique yields more than a factor of two improvement in accuracy as compared to the existing manual technique for vessels with and without stenosis. This technique can also be used to correct for the effect of coronary calcification.