Precise segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms

Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

Segmentation of the striatum from MR brain images to calculate the 99mTc-TRODAT-1 binding ratio in SPECT images

Quantification of regional ^99mTc-TRODAT-1 binding ratio in the striatum regions in SPECT images is essential for differential diagnosis between Alzheimer's and Parkinson's diseases. Defining the region of the striatum in the SPECT image is the first step toward success in the quantification of the TRODAT-1 binding ratio. However, because SPECT images reveal insufficient information regarding the anatomical structure of the brain, correct delineation of the striatum directly from the SPECT image is almost impossible. We present a method integrating the active contour model and the hybrid registration technique to extract regions from MR T1-weighted images and map them into the corresponding SPECT images. Results from three normal subjects suggest that the segmentation accuracy using the proposed method was compatible with the expert decision but has a higher efficiency and reproducibility than manual delineation. The binding ratio derived by this method correlated well (R² = 0.76) with those values calculated by commercial software, suggesting the feasibility of the proposed method.

Automatic model-guided segmentation of the human brain ventricular system from CT images

RATIONALE AND OBJECTIVES

Accurate segmentation of the brain ventricular system on computed tomographic (CT) imaging is useful in neurodiagnosis and neurosurgery. Manual segmentation is time consuming, usually not reproducible, and subjective. Because of image noise, low contrast between soft tissues, large interslice distance, large shape, and size variations of the ventricular system, no automatic method is presently available. The authors propose a model-guided method for the automated segmentation of the ventricular system.

MATERIALS AND METHODS

Fifty CT scans of patients with strokes at different sites were collected for this study. Given a brain CT image, its ventricular system was segmented in five steps: (1) a predefined volumetric model was registered (or deformed) onto the image; (2) according to the deformed model, eight regions of interest were automatically specified; (3) the intensity threshold of cerebrospinal fluid was calculated in a region of interest and used to segment all regions of cerebrospinal fluid from the entire brain volume; (4) each ventricle was segmented in its specified region of interest; and (5) intraventricular calcification regions were identified to refine the ventricular segmentation.

RESULTS

Compared to ground truths provided by experts, the segmentation results of this method achieved an average overlap ratio of 85% for the entire ventricular system. On a desktop personal computer with a dual-core central processing unit running at 2.13 GHz, about 10 seconds were required to analyze each data set.

CONCLUSION

Experiments with clinical CT images showed that the proposed method can generate acceptable results in the presence of image noise, large shape, and size variations of the ventricular system, and therefore it is potentially useful for the quantitative interpretation of CT images in neurodiagnosis and neurosurgery.

Ventricle Boundary in CT: Partial Volume Effect and Local Thresholds

We present a mathematical frame to carry out segmentation of cerebrospinal fluid (CSF) of ventricular region in computed tomography (CT) images in the presence of partial volume effect (PVE). First, the image histogram is fitted using the Gaussian mixture model (GMM). Analyzing the GMM, we find global threshold based on parameters of distributions for CSF, and for the combined white and grey matter (WGM). The parameters of distribution of PVE pixels on the boundary of ventricles are estimated by using a convolution operator. These parameters are used to calculate local thresholds for boundary pixels by the analysis of contribution of the neighbor pixels intensities into a PVE pixel. The method works even in the case of an almost unimodal histogram; it can be useful to analyze the parameters of PVE in the ground truth provided by the expert.

Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography

Physicians evaluate computed tomography (CT) of the brain to quantitatively and qualitatively identify various types of intracranial hematomas for patients with neurological emergencies. We propose a novel method that can perform this task in a totally automatic fashion, based on a multiresolution binary level set method. The skull regions are segmented in downsized images generated with a maximum filter. The intracranial regions are located using the average gray levels and connectivity. These regions compose the regions of interest (ROIs) for segmenting the hematoma from the normal brain. The gray levels of the voxels within these ROIs are generated with an averaging filter in a multiresolution fashion. After identifying the candidate hematoma voxels using adaptive thresholds and connectivity, binary level set algorithm is applied repeatedly until the original resolution is reached. We apply our method to non-volumetric non-contrast CT images of 15 surgically proven intracranial hematomas and the results were quantitatively evaluated by a human expert. The correlation coefficient between the volumes measured manually and automatically is 0.97. The overlap metrics ranged from 0.97 to 0.74, with an average of 0.88. The average precision and recall are 0.89 and 0.87, respectively. We use decision rules to classify these hematomas and were able to make correct diagnoses in all cases.

A time-saving and facilitating approach for segmentation of anatomically defined cortical regions: MRI volumetry

In this study, we present an accurate, reliable, robust, and time-efficient technique for a semi-automatic segmentation of neuroanatomically defined cortical structures in Magnetic Resonance Imaging (MRI) scans. It involves manual drawing of the border of a region of interest (ROI), supported by three-dimensional (3D) visualization techniques (rendering), and a subsequent automatic tracing of the gray matter voxels inside the ROI by means of an automatic tissue classifier. The approach has been evaluated on a set of MRI scans of 75 participants selected from the Maastricht Aging Study (MAAS) and applied to cortical brain structures for both the left and right hemispheres, viz., the inferior prefrontal cortex (PFC); the orbital PFC; the dorsolateral PFC; the anterior cingulate cortex; and the posterior cingulate cortex. The use of a 3D surface-rendered brain can be rotated in any direction was invaluable in identifying anatomical landmarks on the basis of gyral and sulcal topography. This resulted in a high accuracy (anatomical correctness) and reliability: the intra-rater intra-class correlation coefficient (ICC) was between 0.96 and 0.99. Furthermore, the obtained time savings were substantial, i.e., up to a factor of 7.5 compared with fully manual segmentations.

Cerebellum, language, and cognition in autism and specific language impairment

We performed cerebellum segmentation and parcellation on magnetic resonance images from right-handed boys, aged 6-13 years, including 22 boys with autism [16 with language impairment (ALI)], 9 boys with Specific Language Impairment (SLI), and 11 normal controls. Language-impaired groups had reversed asymmetry relative to unimpaired groups in posterior-lateral cerebellar lobule VIIIA (right side larger in unimpaired groups, left side larger in ALI and SLI), contralateral to previous findings in inferior frontal cortex language areas. Lobule VIIA Crus I was smaller in SLI than in ALI. Vermis volume, particularly anterior I-V, was decreased in language-impaired groups. Language performance test scores correlated with lobule VIIIA asymmetry and with anterior vermis volume. These findings suggest ALI and SLI subjects show abnormalities in neurodevelopment of fronto-corticocerebellar circuits that manage motor control and the processing of language, cognition, working memory, and attention.

Three-dimensional coupled-object segmentation using symmetry and tissue type information

This paper presents an automatic method for segmentation of brain structures using their symmetry and tissue type information. The proposed method generates segmented structures that have homogenous tissues. It benefits from general symmetry of the brain structures in the two hemispheres. It also benefits from the tissue regions generated by fuzzy c-means clustering. All in all, the proposed method can be described as a dynamic knowledge-based method that eliminates the need for statistical shape models of the structures while generating accurate segmentation results. The proposed approach is implemented in MATLAB and tested on the Internet Brain Segmentation Repository (IBSR) datasets. To this end, it is applied to the segmentation of caudate and ventricles three-dimensionally in magnetic resonance images (MRI) of the brain. Impacts of each of the steps of the proposed approach are demonstrated through experiments. It is shown that the proposed method generates accurate segmentation results that are insensitive to initialization and parameter selection. The proposed method is compared to four previous methods illustrating advantages and limitations of each method.

Automatic segmentation of the human brain ventricles from MR images by knowledge-based region growing and trimming

Automatic segmentation of the human brain ventricular system from MR images is useful in studies of brain anatomy and its diseases. Existing intensity-based segmentation methods are adaptive to large shape and size variations of the ventricular system, but may leak to the non-ventricular regions due to the non-homogeneity, noise and partial volume effect in the images. Deformable model-based methods are more robust to noise and alleviate the leakage problem, but may generate wrong results when the shape or size of the ventricle to be segmented in the images has a large difference in comparison to its model. In this paper, we propose a knowledge-based region growing and trimming approach where: (1) a model of a ventricular system is used to define regions of interest (ROI) for the four ventricles (i.e., left, right, third and fourth); (2) to segment a ventricle in its ROI, a region growing procedure is first applied to obtain a connected region that contains the ventricle, and (3) a region trimming procedure is then employed to trim the non-ventricle regions. A hysteretic thresholding is developed for the region growing procedure to cope with the partial volume effect and minimize non-ventricular regions. The domain knowledge on the shape and intensity features of the ventricular system is used for the region trimming procedure. Due to the joint use of the model-based and intensity-based approaches, our method is robust to noise and large shape and size variations. Experiments on 18 simulated and 58 clinical MR images show that the proposed approach is able to segment the ventricular system accurately with the dice similarity coefficient ranging from 91% to 99%.

Automatic segmentation of the caudate nucleus from human brain MR images

We describe a knowledge-driven algorithm to automatically delineate the caudate nucleus (CN) region of the human brain from a magnetic resonance (MR) image. Since the lateral ventricles (LVs) are good landmarks for positioning the CN, the algorithm first extracts the LVs, and automatically localizes the CN from this information guided by anatomic knowledge of the structure. The face validity of the algorithm was tested with 55 high-resolution T1-weighted magnetic resonance imaging (MRI) datasets, and segmentation results were overlaid onto the original image data for visual inspection. We further evaluated the algorithm by comparing automated segmentation results to a "gold standard" established by human experts for these 55 MR datasets. Quantitative comparison showed a high intraclass correlation between the algorithm and expert as well as high spatial overlap between the regions-of-interest (ROIs) generated from the two methods. The mean spatial overlap +/- standard deviation (defined by the intersection of the 2 ROIs divided by the union of the 2 ROIs) was equal to 0.873 +/- 0.0234. The algorithm has been incorporated into a public domain software program written in Java and, thus, has the potential to be of broad benefit to neuroimaging investigators interested in basal ganglia anatomy and function.

Segmentation of subcortical brain structures using fuzzy templates

We propose a novel method to automatically segment subcortical structures of human brain in magnetic resonance images by using fuzzy templates. A set of fuzzy templates of the structures based on features such as intensity, spatial location, and relative spatial relationship among structures are first created from a set of training images by defining the fuzzy membership functions and by fusing the information of features. Segmentation is performed by registering the fuzzy templates of the structures on the test image and then by fusing them with the tissue maps of the test image. The final decision is taken in order to optimize the certainty in the intensity, location, relative position, and tissue content of the structure. Our method does not require specific expert definition of each structure or manual interactions during segmentation process. The technique is demonstrated with the segmentation of five structures: thalamus, putamen, caudate, hippocampus, and amygdala; the performance of the present method is comparable with previous techniques.

Language-association cortex asymmetry in autism and specific language impairment

Language deficits are among the core impairments of autism. We previously reported asymmetry reversal of frontal language cortex in boys with autism. Specific language impairment (SLI) and autism share similar language deficits and may share genetic links. This study evaluated asymmetry of frontal language cortex in a new, independent sample of right-handed boys, including a new sample of boys with autism and a group of boys with SLI. The boys with autism were divided into those with language impairment (ALI) and those with normal language ability (ALN). Subjects (right-handed, aged 6.2-13.4 years) included 22 boys with autism (16 ALI and 6 ALN), 9 boys with a history of or present SLI, and 11 normal controls. MRI brain scans were segmented into grey and white matter; then the cerebral cortex was parcellated into 48 gyral-based divisions per hemisphere. Group differences in volumetric asymmetry were predicted a priori in language-related regions in inferior lateral frontal (Broca's area) and posterior superior temporal cortex. Language impaired boys with autism and SLI both had significant reversal of asymmetry in frontal language-related cortex; larger on the right side in both groups of language impaired boys and larger on the left in both unimpaired language groups, strengthening a phenotypic link between ALI and SLI. Thus, we replicated the observation of reversed asymmetry in frontal language cortex reported previously in an independent autism sample, and observed similar reversal in boys with SLI, further strengthening a phenotypic link between SLI and a subgroup of autism. Linguistically unimpaired boys with autism had similar asymmetry compared with the control group, suggesting that Broca's area asymmetry reversal is related more to language impairment than specifically to autism diagnosis.

Knowledge-driven automated extraction of the human cerebral ventricular system from MR images

This work presents an efficient and automated method to extract the human cerebral ventricular system from MRI driven by anatomic knowledge. The ventricular system is divided into six three-dimensional regions; six ROIs are defined based on the anatomy and literature studies regarding variability of the cerebral ventricular system. The distribution histogram of radiological properties is calculated in each ROI, and the intensity thresholds for extracting each region are automatically determined. Intensity inhomogeneities are accounted for by adjusting intensity threshold to match local situation. The extracting method is based on region-growing and anatomical knowledge, and is designed to include all ventricular parts, even if they appear unconnected on the image. The ventricle extraction method was implemented on the Window platform using C++, and was validated qualitatively on 30 MRI studies with variable parameters.

A knowledge-driven algorithm for a rapid and automatic extraction of the human cerebral ventricular system from MR neuroimages

A knowledge-driven algorithm for a rapid, robust, accurate, and automatic extraction of the human cerebral ventricular system from MR neuroimages is proposed. Its novelty is in combination of neuroanatomy, radiological properties, and variability of the ventricular system with image processing techniques. The ventricular system is divided into six 3D regions: bodies and inferior horns of the lateral ventricles, third ventricle, and fourth ventricle. Within each ventricular region, a 2D region of interest (ROI) is defined based on anatomy and variability. Each ventricular region is further subdivided into subregions, and conditions detecting and preventing leakage into the extra-ventricular space are specified for each subregion. The algorithm extracts the ventricular system by (1) processing each ROI (to calculate its local statistics, determine local intensity ranges of cerebrospinal fluid and gray and white matters, set a seed point within the ROI, grow region directionally in 3D, check anti-leakage conditions, and correct growing if leakage occurred) and (2) connecting all unconnected regions grown by relaxing growing conditions. The algorithm was validated qualitatively on 68 and quantitatively on 38 MRI normal and pathological cases (30 clinical, 20 MGH Brain Repository, and 18 MNI BrainWeb data sets). It runs successfully for normal and pathological cases provided that the slice thickness is less than 3.0 mm in axial and less than 2.0 mm in coronal directions, and the data do not have a high inter-slice intensity variability. The algorithm also works satisfactorily in the presence of up to 9% noise and up to 40% RF inhomogeneity for the BrainWeb data. The running time is less than 5 s on a Pentium 4, 2.0 GHz PC. The best overlap metric between the results of a radiology expert and the algorithm is 0.9879 and the worst 0.9527; the mean and standard deviation of the overlap metric are 0.9723 and 0.01087, respectively.

Automatic volumetric measurement of lateral ventricles on magnetic resonance images with correction of partial volume effects

PURPOSE

To propose a method for the quantification of lateral ventricle (LV) volumes on a single sequence of 3D magnetic resonance (MR) images.

MATERIALS AND METHODS

This algorithm, following a preliminary fuzzy tissue classification step, is based on the development of mathematical morphology processes allowing both the extraction of the LVs and the correction of partial volume effects on their boundaries. The procedure is fast and totally unsupervised. The method is tested on a phantom image, then applied to five patients diagnosed as potentially suffering from Alzheimer's disease, and finally applied on several MR acquisitions to show the genericness of the algorithm.

RESULTS AND CONCLUSION

This technique yielded both an accurate estimation of ventricular volumes intra- and intersubject with respect to published data and a relevant management of partial volume effects. Numerous clinical applications are now expected, from the study of schizophrenia to the longitudinal follow-up of Alzheimer's patients.

Automatic segmentation of the ventricular system from MR images of the human brain

An algorithm was developed that automatically segments the lateral and third ventricles from T1-weighted 3-D-FFE MR images of the human brain. The algorithm is based upon region-growing and mathematical morphology operators and starts from a coarse binary total brain segmentation, which is obtained from the 3-D-FFE image. Anatomical knowledge of the ventricular system has been incorporated into the method in order to find all constituting parts of the system, even if they are disconnected, and to avoid inclusion of nonventricle cerebrospinal fluid (CSF) regions. A test of the method on a synthetic MR brain image produced a segmentation overlap of 0.98 between the simulated ventricles ("model") and those defined by the algorithm. Further tests were performed on a large data set of 227 1.5 T MR brain images. The algorithm yielded useful results for 98% of the images. The automatic segmentations had intra-class correlation coefficients of 0.996 for the lateral ventricles and 0.86 for the third ventricle, with manually edited segmentations. Comparison of ventricular volumes of schizophrenia patients compared with those of healthy control subjects showed results in agreement with the literature.

Automatic segmentation of subcortical brain structures in MR images using information fusion

This paper reports a new automated method for the segmentation of internal cerebral structures using an information fusion technique. The information is provided both by images and expert knowledge, and consists in morphological, topological, and tissue constitution data. All this ambiguous, complementary and redundant information is managed using a three-step fusion scheme based on fuzzy logic. The information is first modeled into a common theoretical frame managing its imprecision and incertitude. The models are then fused and a decision is taken in order to reduce the imprecision and to increase the certainty in the location of the structures. The whole process is illustrated on the segmentation of thalamus, putamen, and head of the caudate nucleus from expert knowledge and magnetic resonance images, in a protocol involving 14 healthy volunteers. The quantitative validation is achieved by comparing computed, manually segmented structures and published data by means of indexes assessing the accuracy of volume estimation and spatial location. Results suggest a consistent volume estimation with respect to the expert quantification and published data, and a high spatial similarity of the segmented and computed structures. This method is generic and applicable to any structure that can be defined by expert knowledge and morphological images.

Three-dimensional segmentation of anatomical structures in MR images on large data bases

In this paper an image-based method founded on mathematical morphology is presented in order to facilitate the segmentation of cerebral structures over large data bases of 3D magnetic resonance images (MRIs). The segmentation is described as an immersion simulation, applied to the modified gradient image, modeled by a generated 3D-region adjacency graph (RAG). The segmentation relies on two main processes: homotopy modification and contour decision. The first one is achieved by a marker extraction stage where homogeneous 3D-regions are identified. This stage uses contrasted regions from morphological reconstruction and labeled flat regions constrained by the RAG. Then, the decision stage intends to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a 3D extension of the watershed transform. The method has been applied on a data base of 3D brain MRIs composed of fifty patients. Results are illustrated by segmenting the ventricles, corpus callosum, cerebellum, hippocampus, pons, medulla and midbrain on our data base and the approach is validated on two phantom 3D MRIs.

Random-grid stereologic volumetry of MR head scans

Point count stereology is a useful tool in obtaining volumetric measures of objects in three-dimensional (3D) images when the segmentation of objects is not feasible. Presently, fixed-grid 3D stereology is being used where a 3D parallelepiped grid is randomly placed for sampling the image space in order to generate test points. Although this is a popular technique, the use of a fixed grid introduces errors in the final estimate in practice and makes the technique inefficient. Random-grid 3D stereology is introduced to improve the efficiency of the volume estimates in stereology. In this manuscript, we prove random-grid stereology as a more consistent technique than fixed-grid stereology and use it for volumetry of the brain and ventricles in magnetic resonance (MR) head scans. We demonstrate superior efficiency and accuracy of random-grid stereology with experiments. Also, the effects of grid sizes, the optimal directions of sectioning the object for volume estimates of the brain and ventricles, and the reliability of the technique are investigated. J. Magn. Reson. Imaging 2000;12:833-841.

Automatic measurement of changes in brain volume on consecutive 3D MR images by segmentation propagation

This article presents a technique to automatically measure changes in the volume of a structure of interest in successive 3D magnetic resonance (MR) images and its application in the study of the brain and lateral cerebral ventricles. The only manual step is a segmentation of the structure of interest in the first image. The analysis comprises, first, precise rigid co-registration of the time series of images; second, computation of residual deformations between pairs of images; third, automatic quantification of the volume change, obtained by propagation of the segmentation of the structure of interest through the series of MR images. This approach has been applied to monitor changes in the volume of the brain and lateral cerebral ventricles in a healthy subject and a patient with primary progressive aphasia (PPA). Results are consistent with those obtained by application of the boundary shift integral (BSI) and by stereology in the same subjects.