MRI brain image segmentation by multi-resolution edge detection and region selection

Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives

A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.

Single planar photonic chip with tailored angular transmission for multiple-order analog spatial differentiator

Analog spatial differentiation is used to realize edge-based enhancement, which plays an important role in data compression, microscopy, and computer vision applications. Here, a planar chip made from dielectric multilayers is proposed to operate as both first- and second-order spatial differentiator without any need to change the structural parameters. Third- and fourth-order differentiations that have never been realized before, are also experimentally demonstrated with this chip. A theoretical analysis is proposed to explain the experimental results, which furtherly reveals that more differentiations can be achieved. Taking advantages of its differentiation capability, when this chip is incorporated into conventional imaging systems as a substrate, it enhances the edges of features in optical amplitude and phase images, thus expanding the functions of standard microscopes. This planar chip offers the advantages of a thin form factor and a multifunctional wave-based analogue computing ability, which will bring opportunities in optical imaging and computing.

Reducing variations in multi-center Alzheimer's disease classification with convolutional adversarial autoencoder

Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and brain region-of-interest (ROI) definitions pose a big challenge for multi-center Alzheimer's disease characterization and classification. Existing approaches to reduce such variations require intricate multi-step, often manual preprocessing pipelines, including skull stripping, segmentation, registration, cortical reconstruction, and ROI outlining. Such procedures are time-consuming, and more importantly, tend to be user biased. Contrasting costly and biased preprocessing pipelines, the question arises whether we can design a deep learning model to automatically reduce these variations from multiple centers for Alzheimer's disease classification? In this study, we used T1 and T2-weighted structural MRI from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on three groups with 375 subjects, respectively: patients with Alzheimer's disease (AD) dementia, with mild cognitive impairment (MCI), and healthy controls (HC); to test our approach, we defined AD classification as classifying an individual's structural image to one of the three group labels. We first introduced a convolutional adversarial autoencoder (CAAE) to reduce the variations existing in multi-center raw MRI scans by automatically registering them into a common aligned space. Afterward, a convolutional residual soft attention network (CRAT) was further proposed for AD classification. Canonical classification procedures demonstrated that our model achieved classification accuracies of 91.8%, 90.05%, and 88.10% for the 2-way classification tasks using the RAW aligned MRI scans, including AD vs. HC, AD vs. MCI, and MCI vs. HC, respectively. Thus, our automated approach achieves comparable or even better classification performance by comparing it with many baselines with dedicated conventional preprocessing pipelines. Furthermore, the uncovered brain hotpots, i.e., hippocampus, amygdala, and temporal pole, are consistent with previous studies.

Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks

¹⁸F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.

Whole brain segmentation with full volume neural network

Whole brain segmentation is an important neuroimaging task that segments the whole brain volume into anatomically labeled regions-of-interest. Convolutional neural networks have demonstrated good performance in this task. Existing solutions, usually segment the brain image by classifying the voxels, or labeling the slices or the sub-volumes separately. Their representation learning is based on parts of the whole volume whereas their labeling result is produced by aggregation of partial segmentation. Learning and inference with incomplete information could lead to sub-optimal final segmentation result. To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume. The framework makes use of complete information in each volume and can be implemented easily. An effective instance in this framework is given subsequently. We adopt the 3D high-resolution network (HRNet) for learning spatially fine-grained representations and the mixed precision training scheme for memory-efficient training. Extensive experiment results on a publicly available 3D MRI brain dataset show that our proposed model advances the state-of-the-art methods in terms of segmentation performance.

Classification of retinal images based on convolutional neural network

Automatic detection of maculopathy disease is a very important step to achieve high-accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.

Metasurface enabled quantum edge detection

Metasurfaces consisting of engineered dielectric or metallic structures provide unique solutions to realize exotic phenomena including negative refraction, achromatic focusing, electromagnetic cloaking, and so on. The intersection of metasurface and quantum optics may lead to new opportunities but is much less explored. Here, we propose and experimentally demonstrate that a polarization-entangled photon source can be used to switch ON or OFF the optical edge detection mode in an imaging system based on a high-efficiency dielectric metasurface. This experiment enriches both fields of metasurface and quantum optics, representing a promising direction toward quantum edge detection and image processing with remarkable signal-to-noise ratio.

Review on 2D and 3D MRI Image Segmentation Techniques

BACKGROUND

Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics.

DISCUSSION

Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation.

CONCLUSION

This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

Two-dimensional optical spatial differentiation and high-contrast imaging

Optical analog signal processing technology has been widely studied and applied in a variety of science and engineering fields, with the advantages of overcoming the low-speed and high-power consumption associated with its digital counterparts. Much attention has been given to emerging metasurface technology in the field of optical imaging and processing systems. Here, we demonstrate, for the first time, broadband two-dimensional spatial differentiation and high-contrast edge imaging based on a dielectric metasurface across the whole visible spectrum. This edge detection method works for both intensity and phase objects simply by inserting the metasurface into a commercial optical microscope. This highly efficient metasurface performing a basic optical differentiation operation opens up new opportunities in applications of fast, compactible and power-efficient ultrathin devices for data processing and biological imaging.

Automated brain tumor segmentation from multi-slices FLAIR MRI images

Brain tumors are considered to be a leading cause of cancer death among young people. Early diagnosis is thus essential for treatment. The brain segmentation process is still challenging due to complexity and variation of the tumor structure, intensity similarity between tumor tissues and normal brain tissues. In this paper, a fully automated and reliable brain tumor segmentation system is proposed. This system is able to detect range of slices from a volume that is likely to contain tumor in MRI images. An iterated k-means algorithm is used for the segmentation process in conjunction with a cluster validity index to select the optimal number of clusters. The proposed approach is evaluated using simulated and real MRI of human brain from multimodal brain tumor image segmentation benchmark (BRATS) organized by MICCAI 2012 challenge. Our results achieved average for Dice overlap and Jaccard index for complete tumor region of 91.96% and 98.31% respectively when testing a set of 77 volumes. This shows the robustness of the new technique for clinical routine use.

Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.

Multiple regression analysis of the craniofacial region of Chinese Han people using linear and angular measurements based on MRI

The purpose of this study was to measure the craniofacial region of Chinese Han people in the linear and angular dimensions, and to analyse the effects on sex, age and body parameters (height and weight). All 250 individuals (86 males, 164 females) underwent a three-dimensional magnetic resonance imaging (MRI) scan, and the MRI data were imported into VG Studio MAX 2.2 software. Each linear and angular measurement in the craniofacial region was processed directly. Using SPSS 20.0 software, nine multiple regression equations were constructed, and all the adjusted R² values were statistically significant (0.031–0.311). Multiple regression analysis showed that most craniofacial measurements of Chinese people were significantly correlated with height, weight or age. The multiple regression equations constructed will be helpful in anthropometric analysis and forensic inference.

Assessment of Glioma Response to Radiotherapy Using Multiple MRI Biomarkers with Manual and Semiautomated Segmentation Algorithms

BACKGROUND AND PURPOSE

Multimodality magnetic resonance imaging (MRI) can provide complementary information in the assessment of brain tumors. We aimed to segment tumor in amide proton transfer-weighted (APTw) images and to investigate multiparametric MRI biomarkers for the assessment of glioma response to radiotherapy. For tumor extraction, we evaluated a semiautomated segmentation method based on region of interest (ROI) results by comparing it with the manual segmentation method.

METHODS

Thirteen nude rats injected with U87 tumor cells were irradiated by an 8-Gy radiation dose. All MRI scans were performed on a 4.7-T animal scanner preradiation, and at day 1, day 4, and day 8 postradiation. Two experts performed manual and semiautomated methods to extract tumor ROIs on APTw images. Multimodality MRI signals of the tumors, including structural (T₂ and T₁ ), functional (apparent diffusion coefficient and blood flow), and molecular (APTw and magnetization transfer ratio or MTR), were calculated and compared quantitatively.

RESULTS

The semiautomated method provided more reliable tumor extraction results on APTw images than the manual segmentation, in less time. A considerable increase in the ADC intensities of the tumor was observed during the postradiation. A steady decrease in the blood flow values and in the APTw signal intensities were found after radiotherapy.

CONCLUSIONS

The semiautomated method of tumor extraction showed greater efficiency and stability than the manual method. Apparent diffusion coefficient, blood flow, and APTw are all useful biomarkers in assessing glioma response to radiotherapy.

Functional nanoparticles for magnetic resonance imaging

Nanoparticle-based magnetic resonance imaging (MRI) contrast agents have received much attention over the past decade. By virtue of a high payload of magnetic moieties, enhanced accumulation at disease sites, and a large surface area for additional modification with targeting ligands, nanoparticle-based contrast agents offer promising new platforms to further enhance the high resolution and sensitivity of MRI for various biomedical applications. T ₂ * superparamagnetic iron oxide nanoparticles (SPIONs) first demonstrated superior improvement on MRI sensitivity. The prevailing SPION attracted growing interest in the development of refined nanoscale versions of MRI contrast agents. Afterwards, T ₁ -based contrast agents were developed, and became the most studied subject in MRI due to the positive contrast they provide that avoids the susceptibility associated with MRI signal reduction. Recently, chemical exchange saturation transfer (CEST) contrast agents have emerged and rapidly gained popularity. The unique aspect of CEST contrast agents is that their contrast can be selectively turned 'on' and 'off' by radiofrequency saturation. Their performance can be further enhanced by incorporating a large number of exchangeable protons into well-defined nanostructures. Besides activatable CEST contrast agents, there is growing interest in developing nanoparticle-based activatable MRI contrast agents responsive to stimuli (pH, enzyme, etc.), which improves sensitivity and specificity. In this review, we summarize the recent development of various types of nanoparticle-based MRI contrast agents, and have focused our discussions on the key advantages of introducing nanoparticles in MRI. WIREs Nanomed Nanobiotechnol 2016, 8:814-841. doi: 10.1002/wnan.1400 For further resources related to this article, please visit the WIREs website.

SEGMENTATION OF BRAIN MRIs BY SUPPORT VECTOR MACHINE: DETECTION AND CHARACTERIZATION OF STROKES

The aim of our work is the segmentation of healthy and pathological brains to obtain brain structures and extract strokes. We used real magnetic resonance (MR) images weighted on diffusion. The brain was isolated, and the images were filtered by an anisotropic filter, and then segmented by support vector machines (SVMs). We first applied the method on synthetic images to test the performance of the algorithm and adjust the parameters. Then, we compared our results with those obtained by a cooperative approach proposed in a previous paper.

LoAd: a locally adaptive cortical segmentation algorithm

Thickness measurements of the cerebral cortex can aid diagnosis and provide valuable information about the temporal evolution of diseases such as Alzheimer's, Huntington's, and schizophrenia. Methods that measure the thickness of the cerebral cortex from in-vivo magnetic resonance (MR) images rely on an accurate segmentation of the MR data. However, segmenting the cortex in a robust and accurate way still poses a challenge due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cortical folds. Beginning with a well-established probabilistic segmentation model with anatomical tissue priors, we propose three post-processing refinements: a novel modification of the prior information to reduce segmentation bias; introduction of explicit partial volume classes; and a locally varying MRF-based model for enhancement of sulci and gyri. Experiments performed on a new digital phantom, on BrainWeb data and on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) show statistically significant improvements in Dice scores and PV estimation (p<10(-3)) and also increased thickness estimation accuracy when compared to three well established techniques.

Development of identification of the central sulcus in brain magnetic resonance imaging

Magnetic resonance imaging (MRI) is useful in the quantitative evaluation of brain atrophy, because the superior contrast resolution facilitates separation of the gray and white matter. Quantitative assessment of brain atrophy has mainly been performed by manual measurement, which requires considerable time and effort to determine the brain volume. Therefore, computer-aided quantitative measurement methods for the diagnosis of brain atrophy are required. We have developed a method of segmenting the cerebrum, cerebellum-brainstem, and temporal lobe simultaneously on MR images obtained in a single sequence. It is important to measure the volume of not only these regions but also the frontal lobe in clinical use. However, for segmenting the frontal lobe, it is necessary to identify the Sylvian fissure and the central sulcus, which represent boundaries. Here, we developed a method of identifying the central sulcus from MR images obtained with a 1.5 T MRI scanner. The brain and the cerebrospinal fluid (CSF) regions were segmented using semiautomated segmentation method on MR images. The central sulcus shows an oblique line from the inside to the outside on the convexity view. The almost straight appearance of the central sulcus was used for segmentation of the central sulcus from the segmented CSF images. The central sulcus was identified with this technique in 77% of the images obtained by all sequences. This technique for identifying the central sulcus is very important not only for volumetry, but also for clinical diagnosis.

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%.

Medical images edge detection based on mathematical morphology

Medical images edge detection is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, edge is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image edge detection. In this paper, basic mathematical morphological theory and operations are introduced at first, and then a novel mathematical morphological edge detection algorithm is proposed to detect the edge of lungs CT image with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and edge detection than the usually used template-based edge detection algorithms and general morphological edge detection algorithms.

Probabilistic segmentation of brain tissue in MR imaging

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

An advanced boundary element method (BEM) implementation for the forward problem of electromagnetic source imaging

The forward problem of electromagnetic source imaging has two components: a numerical model to solve the related integral equations and a model of the head geometry. This study is on the boundary element method (BEM) implementation for numerical solutions and realistic head modelling. The use of second-order (quadratic) isoparametric elements and the recursive integration technique increase the accuracy in the solutions. Two new formulations are developed for the calculation of the transfer matrices to obtain the potential and magnetic field patterns using realistic head models. The formulations incorporate the use of the isolated problem approach for increased accuracy in solutions. If a personal computer is used for computations, each transfer matrix is calculated in 2.2 h. After this pre-computation period, solutions for arbitrary source configurations can be obtained in milliseconds for a realistic head model. A hybrid algorithm that uses snakes, morphological operations, region growing and thresholding is used for segmentation. The scalp, skull, grey matter, white matter and eyes are segmented from the multimodal magnetic resonance images and meshes for the corresponding surfaces are created. A mesh generation algorithm is developed for modelling the intersecting tissue compartments, such as eyes. To obtain more accurate results quadratic elements are used in the realistic meshes. The resultant BEM implementation provides more accurate forward problem solutions and more efficient calculations. Thus it can be the firm basis of the future inverse problem solutions.

Magnetic resonance imaging-based volumetry differentiates progressive supranuclear palsy from corticobasal degeneration

Because there are no biological markers for the clinical diagnosis of progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD), we established a mathematical model based on three-dimensional magnetic resonance (MR) imaging to differentiate between these parkinsonian disorders. Using MR imaging-based volumetry we studied the pattern of atrophic changes in patients with probable, possible or definite PSP (n = 33) or CBD (n = 18). Patients were compared with 22 controls with similar age. To establish a mathematical model that would allow for differentiation of PSP, CBD and controls we performed a discriminant analysis. We found a significant reduction in average brain, brainstem, midbrain and frontal gray matter volumes in patients with PSP, whereas patients with CBD showed atrophy of parietal cortex and corpus callosum. With the exception of reduced midbrain volumes in PSP, the measured volumes of anatomical structures showed an extensive overlap with the normal range on an individual basis. Using only post mortem confirmed cases of PSP (n = 8) and CBD (n = 7) as well as all controls, the volumes of midbrain, parietal white matter, temporal gray matter, brainstem, frontal white matter and pons were identified to separate best between groups and were used to construct a model with two canonical variables. This model allowed to correctly predict the diagnosis in 95% of controls as well as in 76% of all PSP and 83% of all CBD patients. Similar results were obtained only when patients with a possible and probable diagnosis of PSP and CBD, who were not involved in the development of the discriminant analysis, were classified. 3D-MR imaging-based volumetry may help to differentiate PSP from CBD ante mortem.

Segmentation of magnetic resonance brain images through discriminant analysis

Segmentation (tissue classification) of medical images obtained from a magnetic resonance (MR) system is a primary step in most applications of medical image post-processing. This paper describes nonparametric discriminant analysis methods to segment multispectral MR images of the brain. Starting from routinely available spin-lattice relaxation time, spin-spin relaxation time, and proton density weighted images (T1w, T2w, PDw), the proposed family of statistical methods is based on: (i) a transform of the images into components that are statistically independent from each other; (ii) a nonparametric estimate of probability density functions of each tissue starting from a training set; (iii) a classic Bayes 0-1 classification rule. Experiments based on a computer built brain phantom (brainweb) and on eight real patient data sets are shown. A comparison with parametric discriminant analysis is also reported. The capability of nonparametric discriminant analysis in improving brain tissue classification of parametric methods is demonstrated. Finally, an assessment of the role of multispectrality in classifying brain tissues is discussed.

Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain

We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.