Automated implant segmentation in cone-beam CT using edge detection and particle counting

Purpose

To develop a fully automated, accurate and robust segmentation technique for dental implants on cone-beam CT (CBCT) images.

Methods

A head-size cylindrical polymethyl methacrylate phantom was used, containing titanium rods of 5.15 mm diameter. The phantom was scanned on 17 CBCT devices, using a total of 39 exposure protocols. Images were manually thresholded to verify the applicability of adaptive thresholding and to determine a minimum threshold value \(({T}_{\mathrm{min}})\) . A three-step automatic segmentation technique was developed. Firstly, images were pre-thresholded using \({T}_{\mathrm{min}}\) . Next, edge enhancement was performed by filtering the image with a Sobel operator. The filtered image was thresholded using an iteratively determined fixed threshold \(({T}_{\mathrm{edge}})\) and converted to binary. Finally, a particle counting method was used to delineate the rods. The segmented area of the titanium rods was compared to the actual area, which was corrected for phantom tilting.

Results

Manual thresholding resulted in large variation in threshold values between CBCTs. After applying the edge-enhancing filter, a stable \({T}_{\mathrm{edge}}\) value of 7.5 % was found. Particle counting successfully detected the rods for all but one device. Deviations between the segmented and real area ranged between \(-\) 2.7 and + \(14.4\,\hbox {mm}^{2}\) with an average absolute error of \(2.8\,\hbox {mm}^{2}\) . Considering the diameter of the segmented area, submillimeter accuracy was seen for all but two data sets.

Conclusion

A segmentation technique was defined which can be applied to CBCT data for an accurate and fully automatic delineation of titanium rods. The technique was validated in vitro and will be further tested and refined on patient data.     

Automated segmentation of the left ventricle in cardiac MRI

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation.     

Automated segmentation of the actively stained mouse brain using multi-spectral MR microscopy

Magnetic resonance microscopy (MRM) has created new approaches for high-throughput morphological phenotyping of mouse models of diseases. Transgenic and knockout mice serve as a test bed for validating hypotheses that link genotype to the phenotype of diseases, as well as developing and tracking treatments. We describe here a Markov random fields based segmentation of the actively stained mouse brain, as a prerequisite for morphological phenotyping. Active staining achieves higher signal to noise ratio (SNR) thereby enabling higher resolution imaging per unit time than obtained in previous formalin-fixed mouse brain studies. The segmentation algorithm was trained on isotropic 43-mum T1- and T2-weighted MRM images. The mouse brain was segmented into 33 structures, including the hippocampus, amygdala, hypothalamus, thalamus, as well as fiber tracts and ventricles. Probabilistic information used in the segmentation consisted of (a) intensity distributions in the T1- and T2-weighted data, (b) location, and (c) contextual priors for incorporating spatial information. Validation using standard morphometric indices showed excellent consistency between automatically and manually segmented data. The algorithm has been tested on the widely used C57BL/6J strain, as well as on a selection of six recombinant inbred BXD strains, chosen especially for their largely variant hippocampus.     

FLAIR序列在脑肿瘤磁共振诊断中的应用

目的 探讨FIAIR序列在脑肿瘤诊断中的应用价值。方法 脑肿瘤患者40例,男27例,女13例。所有病例采用常规TSET1W,T2W和FLAIR序列,其中28例平扫后又行增强,对不同序列显示的肿瘤数目、瘤周水肿、瘤体与水肿的关系、瘤体大小等指标进行研究分析。结果 常规平扫T1W检出肿瘤44个、T2W51个、FLAIR58个,增强28例中检出肿瘤45个。以增强为标准,显示阳性率T1W67％、T2W80％、FLAIR93％。T1W瘤周水肿大部分不清楚,T2W模糊,FLAIR清楚。T1W,T2W、FLAIR测得的瘤周水肿最厚处的累计值分别为298mm、402mm、590mm。FLAIR测得的肿瘤大小亦更接近强化肿瘤的大小。结论 FLAIR是一特殊的IR序列,它借助于抑制脑脊液信号消除脑脊液造成的流动伪影和部分容积效应,使靠近脑室旁、脑表面和脑池内的病灶容易显示,同时对水肿非常敏感;FLAIR在鉴别肿瘤成份、估计肿瘤大小及判断病变转归方面亦优于常规序列,因而FLAIR序列是检查脑肿瘤的好方法。     

动脉瘤性颅内出血的自动检测和血肿分割方法

目的  提出一种端到端的颅内动脉瘤破裂引起的颅内出血多类型血肿全自动分割方法。方法  选择颅内动脉瘤破裂引起的颅内出血644例CT影像数据,按8：2的比例分为训练集和测试集。首先通过区域生长的方式获取脑组织区域,然后利用深度学习对出血区域进行多类型血肿分割。结果  测试集上的结果表明,蛛网膜下腔出血、脑实质出血、脑室内出血和颅内出血的Dice系数分别为62.13%、68.64%、50.08%、71.10%。结论  本文提出的分割网络可以有效地对颅内动脉瘤破裂引起的颅内出血完成多类型血肿分割,配合脑组织提取算法可以在未处理的临床数据上自动完成端到端的处理流程。该方法有效提升颅内动脉瘤破裂引起的颅内出血的诊治效率,有较好的临床应用价值。     

FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping

Fully-automated brain segmentation methods have not been widely adopted for clinical use because of issues related to reliability, accuracy, and limitations of delineation protocol. By combining the probabilistic-based FreeSurfer (FS) method with the Large Deformation Diffeomorphic Metric Mapping (LDDMM)-based label-propagation method, we are able to increase reliability and accuracy, and allow for flexibility in template choice. Our method uses the automated FreeSurfer subcortical labeling to provide a coarse-to-fine introduction of information in the LDDMM template-based segmentation resulting in a fully-automated subcortical brain segmentation method (FS+LDDMM). One major advantage of the FS+LDDMM-based approach is that the automatically generated segmentations generated are inherently smooth, thus subsequent steps in shape analysis can directly follow without manual post-processing or loss of detail. We have evaluated our new FS+LDDMM method on several databases containing a total of 50 subjects with different pathologies, scan sequences and manual delineation protocols for labeling the basal ganglia, thalamus, and hippocampus. In healthy controls we report Dice overlap measures of 0.81, 0.83, 0.74, 0.86 and 0.75 for the right caudate nucleus, putamen, pallidum, thalamus and hippocampus respectively. We also find statistically significant improvement of accuracy in FS+LDDMM over FreeSurfer for the caudate nucleus and putamen of Huntington's disease and Tourette's syndrome subjects, and the right hippocampus of Schizophrenia subjects.     

Neonatal brain image segmentation in longitudinal MRI studies

In the study of early brain development, tissue segmentation of neonatal brain MR images remains challenging because of the insufficient image quality due to the properties of developing tissues. Among various brain tissue segmentation algorithms, atlas-based brain image segmentation can potentially achieve good segmentation results on neonatal brain images. However, their performances rely on both the quality of the atlas and the spatial correspondence between the atlas and the to-be-segmented image. Moreover, it is difficult to build a population atlas for neonates due to the requirement of a large set of tissue-segmented neonatal brain images. To combat these obstacles, we present a longitudinal neonatal brain image segmentation framework by taking advantage of the longitudinal data acquired at late time-point to build a subject-specific tissue probabilistic atlas. Specifically, tissue segmentation of the neonatal brain is formulated as two iterative steps of bias correction and probabilistic-atlas-based tissue segmentation, along with the longitudinal atlas reconstructed by the late time image of the same subject. The proposed method has been evaluated qualitatively through visual inspection and quantitatively by comparing with manual delineations and two population-atlas-based segmentation methods. Experimental results show that the utilization of a subject-specific probabilistic atlas can substantially improve tissue segmentation of neonatal brain images.     

Expert knowledge-guided segmentation system for brain MRI

We describe an automated 3-D segmentation system for in vivo brain magnetic resonance images (MRI). Our segmentation method combines a variety of filtering, segmentation, and registration techniques and makes maximum use of the available a priori biomedical expertise, both in an implicit and an explicit form. We approach the issue of boundary finding as a process of fitting a group of deformable templates (simplex mesh surfaces) to the contours of the target structures. These templates evolve in parallel, supervised by a series of rules derived from analyzing the template's dynamics and from medical experience. The templates are also constrained by knowledge on the expected textural and shape properties of the target structures. We apply our system to segment four brain structures (corpus callosum, ventricles, hippocampus, and caudate nuclei) and discuss its robustness to imaging characteristics and acquisition noise.     

Infant brain probability templates for MRI segmentation and normalization

Spatial normalization and segmentation of infant brain MRI data based on adult or pediatric reference data may not be appropriate due to the developmental differences between the infant input data and the reference data. In this study we have constructed infant templates and a priori brain tissue probability maps based on the MR brain image data from 76 infants ranging in age from 9 to 15 months. We employed two processing strategies to construct the infant template and a priori data: one processed with and one without using a priori data in the segmentation step. Using the templates we constructed, comparisons between the adult templates and the new infant templates are presented. Tissue distribution differences are apparent between the infant and adult template, particularly in the gray matter (GM) maps. The infant a priori information classifies brain tissue as GM with higher probability than adult data, at the cost of white matter (WM), which presents with lower probability when compared to adult data. The differences are more pronounced in the frontal regions and in the cingulate gyrus. Similar differences are also observed when the infant data is compared to a pediatric (age 5 to 18) template. The two-pass segmentation approach taken here for infant T1W brain images has provided high quality tissue probability maps for GM, WM, and CSF, in infant brain images. These templates may be used as prior probability distributions for segmentation and normalization; a key to improving the accuracy of these procedures in special populations.     

Fetal brain volumetry through MRI volumetric reconstruction and segmentation

Purpose  

Fetal MRI volumetry is a useful technique but it is limited by a dependency upon motion-free scans, tedious manual segmentation, and spatial inaccuracy due to thick-slice scans. An image processing pipeline that addresses these limitations was developed and tested.     

Automated model-based vertebra detection,identification, and segmentation in CT images

For many orthopaedic, neurological, and oncological applications, an exact segmentation of the vertebral column including an identification of each vertebra is essential. However, although bony structures show high contrast in CT images, the segmentation and labelling of individual vertebrae is challenging.In this paper, we present a comprehensive solution for automatically detecting, identifying, and segmenting vertebrae in CT images. A framework has been designed that takes an arbitrary CT image, e.g., head-neck, thorax, lumbar, or whole spine, as input and provides a segmentation in form of labelled triangulated vertebra surface models. In order to obtain a robust processing chain, profound prior knowledge is applied through the use of various kinds of models covering shape, gradient, and appearance information. The framework has been tested on 64 CT images even including pathologies. In 56 cases, it was successfully applied resulting in a final mean point-to-surface segmentation error of 1.12 ± 1.04 mm.One key issue is a reliable identification of vertebrae. For a single vertebra, we achieve an identification success of more than 70%. Increasing the number of available vertebrae leads to an increase in the identification rate reaching 100% if 16 or more vertebrae are shown in the image.     

低场MRI快速FLAIR序列在颅脑中的应用

目的探讨低场MRI快速液体衰减反转恢复（fluid attenuated inversion recovery，FLAIR）序列在脑部疾病中的应用价值。方法对185例脑部疾病患者同时行常规MR T2加权像及快速FLAIR检查，比较两种序列对病灶的显示情况。结果FLAIR共检出病灶1889个，常规T2加权像检出1343个，FLAIR显示病灶的轮廓更为清晰，病灶与正常脑组织的对比度更高，在显示脑皮层下、脑室旁病灶方面更有优势，可显示常规MRT：加权像未能显示的侧裂池及脑表面脑沟的小出血病灶，在诊断颅脑外伤及蛛网膜下腔出血方面具有明显的优势。结论快速FLAIR可作为颅脑MR检查常规序列的补充。对于颅脑大部分疾病应行FLAIR检查，以提高病变的检出率，扩大MRI检查范围，减少漏诊发生。     

In vivo detection of neuroarchitecture in the rodent brain using manganese-enhanced MRI

Visualizing brain anatomy in vivo could provide insight into normal and pathophysiology. Here it is demonstrated that neuroarchitecture can be detected in the rodent brain using MRI after systemic MnCl2. Administration of MnCl2 leads to rapid T1 enhancement in the choroid plexus and circumventricular organs, which spreads to the CSF space in ventricles and periventricular tissue. After 1 day, there was MRI enhancement throughout the brain with high intensity in the pituitary, olfactory bulb, cortex, basal forebrain, hippocampus, basal ganglia, hypothalamus, amygdala, and cerebellum. Contrast obtained enabled visualization of specific features of neuroarchitecture. The arrowhead structure of the dentate gyrus as well as the CA1-CA3 region of the hippocampus and layers in cortex, cerebellum, as well as the olfactory bulb could be readily observed. Preliminary assignments of olfactory bulb layers, cortical layers in frontal and somatosensory cortex, and cerebellum were made. Systemic MnCl2 leads to MRI visualization of neuroarchitecture nondestructively.     

Automatic anatomical brain MRI segmentation combining label propagation and decision fusion

Regions in three-dimensional magnetic resonance (MR) brain images can be classified using protocols for manually segmenting and labeling structures. For large cohorts, time and expertise requirements make this approach impractical. To achieve automation, an individual segmentation can be propagated to another individual using an anatomical correspondence estimate relating the atlas image to the target image. The accuracy of the resulting target labeling has been limited but can potentially be improved by combining multiple segmentations using decision fusion. We studied segmentation propagation and decision fusion on 30 normal brain MR images, which had been manually segmented into 67 structures. Correspondence estimates were established by nonrigid registration using free-form deformations. Both direct label propagation and an indirect approach were tested. Individual propagations showed an average similarity index (SI) of 0.754+/-0.016 against manual segmentations. Decision fusion using 29 input segmentations increased SI to 0.836+/-0.009. For indirect propagation of a single source via 27 intermediate images, SI was 0.779+/-0.013. We also studied the effect of the decision fusion procedure using a numerical simulation with synthetic input data. The results helped to formulate a model that predicts the quality improvement of fused brain segmentations based on the number of individual propagated segmentations combined. We demonstrate a practicable procedure that exceeds the accuracy of previous automatic methods and can compete with manual delineations.     

Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm

An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences. We describe the Graph Cuts algorithm and compare the results of Graph Cuts segmentations against "gold standard" manual segmentations and segmentations produced by three popular software packages used by neuroimagers: BrainVisa, CLASP, and SurfRelax.     

Automated segmentation of neuroanatomical structures in multispectral MR microscopy of the mouse brain

We present the automated segmentation of magnetic resonance microscopy (MRM) images of the C57BL/6J mouse brain into 21 neuroanatomical structures, including the ventricular system, corpus callosum, hippocampus, caudate putamen, inferior colliculus, internal capsule, globus pallidus, and substantia nigra. The segmentation algorithm operates on multispectral, three-dimensional (3D) MR data acquired at 90-microm isotropic resolution. Probabilistic information used in the segmentation is extracted from training datasets of T2-weighted, proton density-weighted, and diffusion-weighted acquisitions. Spatial information is employed in the form of prior probabilities of occurrence of a structure at a location (location priors) and the pairwise probabilities between structures (contextual priors). Validation using standard morphometry indices shows good consistency between automatically segmented and manually traced data. Results achieved in the mouse brain are comparable with those achieved in human brain studies using similar techniques. The segmentation algorithm shows excellent potential for routine morphological phenotyping of mouse models.     

Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images

Current semiautomated magnetic resonance (MR)-based brain segmentation and volume measurement methods are complex and not sufficiently accurate for certain applications. We have developed a simpler, more accurate automated algorithm for whole-brain segmentation and volume measurement in T(1)-weighted, three-dimensional MR images. This histogram-based brain segmentation (HBRS) algorithm is based on histograms and simple morphological operations. The algorithm's three steps are foreground/background thresholding, disconnection of brain from skull, and removal of residue fragments (sinus, cerebrospinal fluid, dura, and marrow). Brain volume was measured by counting the number of brain voxels. Accuracy was determined by applying HBRS to both simulated and real MR data. Comparing the brain volume rendered by HBRS with the volume on which the simulation is based, the average error was 1.38%. By applying HBRS to 20 normal MR data sets downloaded from the Internet Brain Segmentation Repository and comparing them with expert segmented data, the average Jaccard similarity was 0.963 and the kappa index was 0.981. The reproducibility of brain volume measurements was assessed by comparing data from two sessions (four total data sets) with human volunteers. Intrasession variability of brain volumes for sessions 1 and 2 was 0.55 +/- 0.56 and 0.74 +/- 0.56%, respectively; the mean difference between the two sessions was 0.60 +/- 0.46%. These results show that the HBRS algorithm is a simple, fast, and accurate method to determine brain volume with high reproducibility. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving MRI data are needed.     

Automated detection of focal cortical dysplasia lesions using computational models of their MRI characteristics and texture analysis

Focal cortical dysplasia (FCD), a malformation of cortical development, is a frequent cause of pharmacologically intractable epilepsy. FCD is characterized on Tl-weighted MRI by cortical thickening, blurring of the gray-matter/white-matter interface, and gray-level hyperintensity. We have previously used computational models of these characteristics to enhance visual lesion detection. In the present study we seek to improve our methods by combining these models with features derived from texture analysis of MRI, which allows measurement of image properties not readily accessible by visual analysis. These computational models and texture features were used to develop a two-stage Bayesian classifier to perform automated FCD lesion detection. Eighteen patients with histologically confirmed FCD and 14 normal controls were studied. On the MRI volumes of the 18 patients, 20 FCD lesions were manually labeled by an expert observer. Three-dimensional maps of the computational models and texture features were constructed for all subjects. A Bayesian classifier was trained on the computational models to classify voxels as cerebrospinal fluid, gray-matter, white-matter, transitional, or lesional. Voxels classified as lesional were subsequently reclassified based on the texture features. This process produced a 3D lesion map, which was compared to the manual lesion labels. The automated classifier identified 17/20 manually labeled lesions. No lesions were identified in controls. Thus, combining models of the T1-weighted MRI characteristics of FCD with texture analysis enabled successful construction of a classifier. This computer-based, automated method may be useful in the presurgical evaluation of patients with severe epilepsy related to FCD.     

FLAIR序列在颅脑MR成像中的技术探讨

目的：探讨液体衰减反转恢复序列在颅脑MRI上的技术及临床应用。材料与方法：对40例脑部受检查者分别使用FLAIR序列及常规自旋回波（SE）序列进行对比分析。结果：FLAIR序列检出病灶128例,常规自旋回波序列检出病灶86个,前者比后者敏感性高,结论：FLAIR技术对颅脑病变的显示优于SE序列T2加权像,可作为颅脑MR检查常规序列的重要补充。