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.     

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

目的　介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。方法　首先对磁共振颅脑图像进行预处理去掉颅骨和肌肉等非脑组织,只保留大脑组织,然后利用模糊Ｋ－ 均值聚类算法计算脑白质、脑灰质和脑脊液的模糊类属函数。结果　模糊Ｋ－ 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。结论　利用模糊Ｋ－ 均值聚类算法分割磁共振颅脑图像能获得较好的分割效果。     

Skull-stripping magnetic resonance brain images using a model-based level set

The segmentation of brain tissue from nonbrain tissue in magnetic resonance (MR) images, commonly referred to as skull stripping, is an important image processing step in many neuroimage studies. A new mathematical algorithm, a model-based level set (MLS), was developed for controlling the evolution of the zero level curve that is implicitly embedded in the level set function. The evolution of the curve was controlled using two terms in the level set equation, whose values represented the forces that determined the speed of the evolving curve. The first force was derived from the mean curvature of the curve, and the second was designed to model the intensity characteristics of the cortex in MR images. The combination of these forces in a level set framework pushed or pulled the curve toward the brain surface. Quantitative evaluation of the MLS algorithm was performed by comparing the results of the MLS algorithm to those obtained using expert segmentation in 29 sets of pediatric brain MR images and 20 sets of young adult MR images. Another 48 sets of elderly adult MR images were used for qualitatively evaluating the algorithm. The MLS algorithm was also compared to two existing methods, the brain extraction tool (BET) and the brain surface extractor (BSE), using the data from the Internet brain segmentation repository (IBSR). The MLS algorithm provides robust skull-stripping results, making it a promising tool for use in large, multi-institutional, population-based neuroimaging studies.     

A comprehensive testing protocol for MRI neuroanatomical segmentation techniques: Evaluation of a novel lateral ventricle segmentation method

Although a wide range of approaches have been developed to automatically assess the volume of brain regions from MRI, the reproducibility of these algorithms across different scanners and pulse sequences, their accuracy in different clinical populations and sensitivity to real changes in brain volume have not always been comprehensively examined. Firstly we present a comprehensive testing protocol which comprises 312 freely available MR images to assess the accuracy, reproducibility and sensitivity of automated brain segmentation techniques. Accuracy is assessed in infants, young adults and patients with Alzheimer's disease in comparison to gold standard measures by expert observers using a manual technique based on Cavalieri's principle. The protocol determines the reliability of segmentation between scanning sessions, different MRI pulse sequences and 1.5T and 3T field strengths and examines their sensitivity to small changes in volume using a large longitudinal dataset. Secondly we apply this testing protocol to a novel algorithm for segmenting the lateral ventricles and compare its performance to the widely used FSL FIRST and FreeSurfer methods. The testing protocol produced quantitative measures of accuracy, reliability and sensitivity of lateral ventricle volume estimates for each segmentation method. The novel algorithm showed high accuracy in all populations (intraclass correlation coefficient, ICC>0.95), good reproducibility between MRI pulse sequences (ICC>0.99) and was sensitive to age related changes in longitudinal data. FreeSurfer demonstrated high accuracy (ICC>0.95), good reproducibility (ICC>0.99) and sensitivity whilst FSL FIRST showed good accuracy in young adults and infants (ICC>0.90) and good reproducibility (ICC=0.98), but was unable to segment ventricular volume in patients with Alzheimer's disease or healthy subjects with large ventricles. Using the same computer system, the novel algorithm and FSL FIRST processed a single MRI image in less than 10min while FreeSurfer took approximately 7h. The testing protocol presented enables the accuracy, reproducibility and sensitivity of different algorithms to be compared. We also demonstrate that the novel segmentation algorithm and FreeSurfer are both effective in determining lateral ventricular volume and are well suited for multicentre and longitudinal MRI studies.     

Consistent reconstruction of cortical surfaces from longitudinal brain MR images

Accurate and consistent reconstruction of cortical surfaces from longitudinal human brain MR images is of great importance in studying longitudinal subtle change of the cerebral cortex. This paper presents a novel deformable surface method for consistent and accurate reconstruction of inner, central and outer cortical surfaces from longitudinal brain MR images. Specifically, the cortical surfaces of the group-mean image of all aligned longitudinal images of the same subject are first reconstructed by a deformable surface method, which is driven by a force derived from the Laplace's equation. And then the longitudinal cortical surfaces are consistently reconstructed by jointly deforming the cortical surfaces of the group-mean image to all longitudinal images. The proposed method has been successfully applied to two sets of longitudinal human brain MR images. Both qualitative and quantitative experimental results demonstrate the accuracy and consistency of the proposed method. Furthermore, the reconstructed longitudinal cortical surfaces are used to measure the longitudinal changes of cortical thickness in both normal and diseased groups, where the overall decline trend of cortical thickness has been clearly observed. Meanwhile, the longitudinal cortical thickness also shows its potential in distinguishing different clinical groups.     

Detection of structural changes of the human brain in longitudinally acquired MR images by deformation field morphometry: methodological analysis, validation and application

The progression of neurodegenerative diseases as well as healthy aging is accompanied by structural changes of the brain. These changes are often only subtle when considered over time intervals of several months. Therefore morphometrical techniques for their detection in longitudinally acquired MR images must be highly sensitive, and they require a careful validation. In the present study, a novel processing chain for a longitudinal analysis based on deformation field morphometry is described. Procedures for its quantitative validation are also reported: Deformation fields were computed for the simulation of non-linear, local structural changes of human brains. Applying these deformation fields to "original" MR images yielded deformed MR images. The volume changes defined by the deformation fields represented the standard, against which the results of the longitudinal analysis of each pair of original and deformed MR image were compared. The proposed processing chain enabled to localize and to quantify simulated local atrophies near the cortex as well as in deep brain structures. An exemplary analysis of serial MR images of a patient suffering from an atypical Parkinson syndrome (cortico-basal degeneration, CBD) and healthy control subjects is presented, showing a characteristic pattern of volume changes in the brain of the patient which is strikingly different from the controls' patterns of changes.     

ABCnet: Adversarial bias correction network for infant brain MR images

Automatic correction of intensity nonuniformity (also termed as the bias correction) is an essential step in brain MR image analysis. Existing methods are typically developed for adult brain MR images based on the assumption that the image intensities within the same brain tissue are relatively uniform. However, this assumption is not valid in infant brain MR images, due to the dynamic and regionally-heterogeneous image contrast and appearance changes, which are caused by the underlying spatiotemporally-nonuniform myelination process. Therefore, it is not appropriate to directly use existing methods to correct the infant brain MR images. In this paper, we propose an end-to-end 3D adversarial bias correction network (ABCnet), tailored for direct prediction of bias fields from the input infant brain MR images for bias correction. The “ground-truth” bias fields for training our network are carefully defined by an improved N4 method, which integrates manually-corrected tissue segmentation maps as anatomical prior knowledge. The whole network is trained alternatively by minimizing generative and adversarial losses. To handle the heterogeneous intensity changes, our generative loss includes a tissue-aware local intensity uniformity term to reduce the local intensity variation in the corrected image. Besides, it also integrates two additional terms to enhance the smoothness of the estimated bias field and to improve the robustness of the proposed method, respectively. Comprehensive experiments with different sizes of training datasets have been carried out on a total of 1492 T1w and T2w MR images from neonates, infants, and adults, respectively. Both qualitative and quantitative evaluations on simulated and real datasets consistently demonstrate the superior performance of our ABCnet in both accuracy and efficiency, compared with popularly available methods.     

A Dirichlet process mixture model for brain MRI tissue classification

Accurate classification of magnetic resonance images according to tissue type or region of interest has become a critical requirement in diagnosis, treatment planning, and cognitive neuroscience. Several authors have shown that finite mixture models give excellent results in the automated segmentation of MR images of the human normal brain. However, performance and robustness of finite mixture models deteriorate when the models have to deal with a variety of anatomical structures. In this paper, we propose a nonparametric Bayesian model for tissue classification of MR images of the brain. The model, known as Dirichlet process mixture model, uses Dirichlet process priors to overcome the limitations of current parametric finite mixture models. To validate the accuracy and robustness of our method we present the results of experiments carried out on simulated MR brain scans, as well as on real MR image data. The results are compared with similar results from other well-known MRI segmentation methods.     

A viscous fluid model for multimodal non-rigid image registration using mutual information

We propose a multimodal free-form registration algorithm based on maximization of mutual information. The warped image is modeled as a viscous fluid that deforms under the influence of forces derived from the gradient of the mutual information registration criterion. Parzen windowing is used to estimate the joint intensity probability of the images to be matched. The method is evaluated for non-rigid inter-subject registration of MR brain images. The accuracy of the method is verified using simulated multi-modal MR images with known ground truth deformation. The results show that the root mean square difference between the recovered and the ground truth deformation is smaller than 1 voxel. We illustrate the application of the method for atlas-based brain tissue segmentation in MR images in case of gross morphological differences between atlas and patient images.     

MR脑肿瘤图像分割与矩特征研究

目的 对MR脑肿瘤图像进行分割,并对分割的结果进行矩描述.方法 在分析当前常用的医学图像分割方法的基础上,提出一种基于形变模型的医学图像分割方法,并给出了相应的理论算法模型和实现步骤,最后用Visual C 6.0编程,并对MR脑肿瘤图像进行分割实验.结果 本文分割方法分割边界清晰,总体不确定性较小.结论 本文分割方法切实可行,分割效果较好,为进一步的MR脑肿瘤图像分析和研究提供了一种有效工具.     

Sequence-independent segmentation of magnetic resonance images

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.     

A robust method for extraction and automatic segmentation of brain images

A new protocol is introduced for brain extraction and automatic tissue segmentation of MR images. For the brain extraction algorithm, proton density and T2-weighted images are used to generate a brain mask encompassing the full intracranial cavity. Segmentation of brain tissues into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) is accomplished on a T1-weighted image after applying the brain mask. The fully automatic segmentation algorithm is histogram-based and uses the Expectation Maximization algorithm to model a four-Gaussian mixture for both global and local histograms. The means of the local Gaussians for GM, WM, and CSF are used to set local thresholds for tissue classification. Reproducibility of the extraction procedure was excellent, with average variation in intracranial capacity (TIC) of 0.13 and 0.66% TIC in 12 healthy normal and 33 Alzheimer brains, respectively. Repeatability of the segmentation algorithm, tested on healthy normal images, indicated scan-rescan differences in global tissue volumes of less than 0.30% TIC. Reproducibility at the regional level was established by comparing segmentation results within the 12 major Talairach subdivisions. Accuracy of the algorithm was tested on a digital brain phantom, and errors were less than 1% of the phantom volume. Maximal Type I and Type II classification errors were low, ranging between 2.2 and 4.3% of phantom volume. The algorithm was also insensitive to variation in parameter initialization values. The protocol is robust, fast, and its success in segmenting normal as well as diseased brains makes it an attractive clinical application.     

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.     

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.     

Serial registration of intraoperative MR images of the brain

The increased use of image-guided surgery systems during neurosurgery has brought to prominence the inaccuracies of conventional intraoperative navigation systems caused by shape changes such as those due to brain shift. We propose a method to track the deformation of the brain and update preoperative images using intraoperative MR images acquired at different crucial time points during surgery. We use a deformable surface matching algorithm to capture the deformation of boundaries of key structures (cortical surface, ventricles and tumor) throughout the neurosurgical procedure, and a linear finite element elastic model to infer a volumetric deformation. The boundary data are extracted from intraoperative MR images using a real-time intraoperative segmentation algorithm. The algorithm has been applied to a sequence of intraoperative MR images of the brain exhibiting brain shift and tumor resection. Our results characterize the brain shift after opening of the dura and at the different stages of tumor resection, and brain swelling afterwards. Analysis of the average deformation capture was assessed by comparing landmarks identified manually and the results indicate an accuracy of 0.7+/-0.6 mm (mean+/-S.D.) for boundary surface landmarks, of 0.9+/-0.6 mm for landmarks inside the boundary surfaces, and 1.6+/-0.9 mm for landmarks in the vicinity of the tumor.     

基于MRI的动物肝脏脉管分割与三维重建

目的探索基于磁共振成像的肝脏脉管(血管、胆管)三维重建的方法,以显示肝脏中血管、胆管走行立体图像,从而指导劈离式肝脏移植手术.方法综合利用二维图像的多种平滑和分割方法,得到肝实质和肝脉管二维图像序列,并在此基础上三维重建肝实质和肝脉管.结果由MR图像序列可三维重建清晰的肝实质和肝血管立体图像,并且可得到肝叶、段间血管及胆管的走行及相互关系.结论利用MRI和图像分割以及图像三维重建技术指导劈离式肝移植手术,具有重要的临床意义.     

Consistent segmentation using a Rician classifier

Several popular classification algorithms used to segment magnetic resonance brain images assume that the image intensities, or log-transformed intensities, satisfy a finite Gaussian mixture model. In these methods, the parameters of the mixture model are estimated and the posterior probabilities for each tissue class are used directly as soft segmentations or combined to form a hard segmentation. It is suggested and shown in this paper that a Rician mixture model fits the observed data better than a Gaussian model. Accordingly, a Rician mixture model is formulated and used within an expectation maximization (EM) framework to yield a new tissue classification algorithm called Rician Classifier using EM (RiCE). It is shown using both simulated and real data that RiCE yields comparable or better performance to that of algorithms based on the finite Gaussian mixture model. As well, we show that RiCE yields more consistent segmentation results when used on images of the same individual acquired with different T1-weighted pulse sequences. Therefore, RiCE has the potential to stabilize segmentation results in brain studies involving heterogeneous acquisition sources as is typically found in both multi-center and longitudinal studies.     

Suggestive annotation of brain MR images with gradient-guided sampling

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.     

A brain tumor segmentation framework based on outlier detection

This paper describes a framework for automatic brain tumor segmentation from MR images. The detection of edema is done simultaneously with tumor segmentation, as the knowledge of the extent of edema is important for diagnosis, planning, and treatment. Whereas many other tumor segmentation methods rely on the intensity enhancement produced by the gadolinium contrast agent in the T1-weighted image, the method proposed here does not require contrast enhanced image channels. The only required input for the segmentation procedure is the T2 MR image channel, but it can make use of any additional non-enhanced image channels for improved tissue segmentation. The segmentation framework is composed of three stages. First, we detect abnormal regions using a registered brain atlas as a model for healthy brains. We then make use of the robust estimates of the location and dispersion of the normal brain tissue intensity clusters to determine the intensity properties of the different tissue types. In the second stage, we determine from the T2 image intensities whether edema appears together with tumor in the abnormal regions. Finally, we apply geometric and spatial constraints to the detected tumor and edema regions. The segmentation procedure has been applied to three real datasets, representing different tumor shapes, locations, sizes, image intensities, and enhancement.     

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method

The Gegenbauer image reconstruction method, previously shown to improve the quality of magnetic resonance images, is utilized in this study as a segmentation preprocessing step. It is demonstrated that, for all simulated and real magnetic resonance images used in this study, the Gegenbauer reconstruction method improves the accuracy of segmentation. Although it is more desirable to use the k-space data for the Gegenbauer reconstruction method, only information acquired from MR images is necessary for the reconstruction, making the procedure completely self-contained and viable for all human brain segmentation algorithms.