Distributed local MRF models for tissue and structure brain segmentation

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.

Multi-agent medical image segmentation: A survey

During the last decades, the healthcare area has increasingly relied on medical imaging for the diagnosis of a growing number of pathologies. The different types of medical images are mostly manually processed by human radiologists for diseases detection and monitoring. However, such a procedure is time-consuming and relies on expert judgment. The latter can be influenced by a variety of factors. One of the most complicated image processing tasks is image segmentation. Medical image segmentation consists of dividing the input image into a set of regions of interest, corresponding to body tissues and organs. Recently, artificial intelligence (AI) techniques brought researchers attention with their promising results for the image segmentation automation. Among AI-based techniques are those that use the Multi-Agent System (MAS) paradigm. This paper presents a comparative study of the multi-agent approaches dedicated to the segmentation of medical images, recently published in the literature.

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Accurate brain tissue segmentation of MRI is vital to diagnosis aiding, treatment planning, and neurologic condition monitoring. As an excellent convolutional neural network (CNN), U-Net is widely used in MR image segmentation as it usually generates high-precision features. However, the performance of U-Net is considerably restricted due to the variable shapes of the segmented targets in MRI and the information loss of down-sampling and up-sampling operations. Therefore, we propose a novel network by introducing spatial and channel dimensions-based multi-scale feature information extractors into its encoding-decoding framework, which is helpful in extracting rich multi-scale features while highlighting the details of higher-level features in the encoding part, and recovering the corresponding localization to a higher resolution layer in the decoding part. Concretely, we propose two information extractors, multi-branch pooling, called MP, in the encoding part, and multi-branch dense prediction, called MDP, in the decoding part, to extract multi-scale features. Additionally, we designed a new multi-branch output structure with MDP in the decoding part to form more accurate edge-preserving predicting maps by integrating the dense adjacent prediction features at different scales. Finally, the proposed method is tested on datasets MRbrainS13, IBSR18, and ISeg2017. We find that the proposed network performs higher accuracy in segmenting MRI brain tissues and it is better than the leading method of 2018 at the segmentation of GM and CSF. Therefore, it can be a useful tool for diagnostic applications, such as brain MRI segmentation and diagnosing.

BISON: Brain tissue segmentation pipeline using T1 -weighted magnetic resonance images and a random forest classifier

PURPOSE

Tissue segmentation from T₁ -weighted (T1W) MRI is a critical requirement in many neuroscience and clinical applications. However, accurate tissue segmentation is challenging because of the variabilities in tissue intensity profiles caused by differences in scanner models, acquisition protocols, and age. In addition, many methods assume healthy anatomy and fail in the presence of pathology such as white matter hyperintensities (WMHs). We present BISON (Brain tISsue segmentatiON), a new pipeline for tissue segmentation using a random forest classifier and a set of intensity and location priors based on T1W MRI.

METHODS

BISON was developed and cross-validated using multiscanner manual labels of 72 subjects aged 5 to 96 years. We also assessed the test-retest reliability of BISON on two data sets: 20 subjects with scan/rescan MR images and manual segmentations and 90 scans from a single individual. The results were compared against Atropos, a state-of-the-art commonly used tissue classification method from advanced normalization tools (ANTs).

RESULTS

BISON cross-validation dice kappa values against manual segmentations of 72 MRI volumes yielded κ_GM = 0.88, κ_WM = 0.85, κ_CSF = 0.77, outperforming Atropos (κ_GM = 0.79, κ_WM = 0.84, κ_CSF = 0.64), test-retest values on 20 subjects of κ_GM = 0.94, κ_WM = 0.92, κ_CSF = 0.77 outperforming both manual (κ_GM = 0.92, κ_WM = 0.91, κ_CSF =0.74) and Atropos (κ_GM = 0.87, κ_WM = 0.92, κ_CSF = 0.79). Finally, BISON outperformed Atropos, FAST (fast automated segmentation tool) from the FMRIB (Functional Magnetic Resonance Imaging of the Brain) Software Library, and SPM12 (statistical parametric mapping 12) in the presence of WMHs.

CONCLUSION

BISON can provide accurate and robust segmentations in data from various age ranges and scanner models, making it ideal for performing tissue classification in large multicenter and multiscanner databases.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions

Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.

A segmentation of brain MRI images utilizing intensity and contextual information by Markov random field

BACKGROUND AND OBJECTIVE

Image segmentation is a preliminary and fundamental step in computer aided magnetic resonance imaging (MRI) images analysis. But the performance of most current image segmentation methods is easily depreciated by noise in MRI images. A precise and anti-noise segmentation of MRI images is desired in modern medical image diagnosis.

METHODS

This paper presents a segmentation of MRI images which combines fuzzy clustering and Markov random field (MRF). In order to utilize gray level information sufficiently and alleviate noise disturbance, fuzzy clustering is carried out on the original image and the coarse scale image of multi-scale decomposition. The spatial constraints between neighboring pixels are modeled by a defined potential function in the MRF to reduce the effect of noise and increase the integrity of segmented regions. Spatial constraints and the gray level information refined by Fuzzy C-Means (FCM) algorithm are integrated by maximum a posteriori Markov random field (MAP-MRF). In the proposed method, the fuzzy clustering membership obtained from the original image and the coarse scale image is integrated into the single-site clique potential functions by MAP-MRF. The defined potential functions and the distance weight are introduced to model the neighborhood constraint with MRF.

RESULTS

The experiments are carried out on noised synthetic images, simulated brain MR images and real MR images. The experimental results show that the proposed method has strong robustness and satisfying performance. Meanwhile the method is compared with FCM, FGFCM and FLICM algorithms visually and statistically in the experiments. In the comparison, the proposed method has achieved the best results. In the statistical comparison, the proposed method has an average similarity index of 36.8%, 33.7%, 2.75% increase against FCM, FGFCM and FLICM.

CONCLUSIONS

This paper proposes a MRI segmentation method combining fuzzy clustering and Markov random field. The method is tested in the noised image databases and comparison experiments, which shows that it is a precise and robust MRI segmentation method.

Connecting Markov random fields and active contour models: application to gland segmentation and classification

We introduce a Markov random field (MRF)-driven region-based active contour model (MaRACel) for histological image segmentation. This Bayesian segmentation method combines a region-based active contour (RAC) with an MRF. State-of-the-art RAC models assume that every spatial location in the image is statistically independent, thereby ignoring valuable contextual information among spatial locations. To address this shortcoming, we incorporate an MRF prior into energy term of the RAC. This requires a formulation of the Markov prior consistent with the continuous variational framework characteristic of active contours; consequently, we introduce a continuous analog to the discrete Potts model. Based on the automated segmentation boundary of glands by MaRACel model, explicit shape descriptors are then employed to distinguish prostate glands belonging to Gleason patterns 3 (G3) and 4 (G4). To demonstrate the effectiveness of MaRACel, we compare its performance to the popular models proposed by Chan and Vese (CV) and Rousson and Deriche (RD) with respect to the following tasks: (1) the segmentation of prostatic acini (glands) and (2) the differentiation of G3 and G4 glands. On almost 600 prostate biopsy needle images, MaRACel was shown to have higher average dice coefficients, overlap ratios, sensitivities, specificities, and positive predictive values both in terms of segmentation accuracy and ability to discriminate between G3 and G4 glands compared to the CV and RD models.

Segmenting and validating brain tissue definitions in the presence of varying tissue contrast

We propose a method for segmenting brain tissue as either gray matter or white matter in the presence of varying tissue contrast, which can derive from either differential changes in tissue water content or increasing myelin content of white matter. Our method models the spatial distribution of intensities as a Markov Random Field (MRF) and estimates the parameters for the MRF model using a maximum likelihood approach. Although previously described methods have used similar models to segment brain tissue, accurate model of the conditional probabilities of tissue intensities and adaptive estimates of tissue properties to local intensities generates tissue definitions that are accurate and robust to variations in tissue contrast with age and across illnesses. Robustness to variations in tissue contrast is important to understand normal brain development and to identify the brain bases of neurological and psychiatric illnesses. We used simulated brains of varying tissue contrast to compare both visually and quantitatively the performance of our method with the performance of prior methods. We assessed validity of the cortical definitions by associating cortical thickness with various demographic features, clinical measures, and medication use in our three large cohorts of participants who were either healthy or who had Bipolar Disorder (BD), Autism Spectrum Disorder (ASD), or familial risk for Major Depressive Disorder (MDD). We assessed validity of the tissue definitions using synthetic brains and data for three large cohort of individuals with various neuropsychiatric disorders. Visual inspection and quantitative analyses showed that our method accurately and robustly defined the cortical mantle in brain images with varying contrast. Furthermore, associating the thickness with various demographic and clinical measures generated findings that were novel and supported by histological analyses or were supported by previous MRI studies, thereby validating the cortical definitions generated by the proposed method: (1) Although cortical thickness decreased with age in adolescents, in adults cortical thickness did not correlate significantly with age. Our synthetic data showed that the previously reported thinning of cortex in adults is likely due to decease in tissue contrast, thereby suggesting that the method generated cortical definitions in adults that were invariant to tissue contrast. In adolescents, cortical thinning with age was preserved likely due to widespread dendritic and synaptic pruning, even though the effects of decreasing tissue contrast were minimized. (3) The method generated novel finding of both localized increases and decreases in thickness of males compared to females after controlling for the differing brain sizes, which are supported by the histological analyses of brain tissue in males and females. (4) The proposed method, unlike prior methods, defined thicker cortex in BD individuals using lithium. The novel finding is supported by the studies that showed lithium treatment increased dendritic arborization and neurogenesis, thereby leading to thickening of cortex. (5) In both BD and ASD participants, associations of more severe symptoms with thinner cortex showed that correcting for the effects of tissue contrast preserved the biological consequences of illnesses. Therefore, consistency of the findings across the three large cohorts of participants, in images acquired on either 1.5T or 3T MRI scanners, and with findings from prior histological analyses provides strong evidence that the proposed method generated valid and accurate definitions of the cortex while controlling for the effects of tissue contrast.

Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis

Parkinson’s disease is a progressive neurodegenerative disorder that has a higher probability of occurrence in middle-aged and older adults than in the young. With the use of a computer-aided diagnosis (CAD) system, abnormal cell regions can be identified, and this identification can help medical personnel to evaluate the chance of disease. This study proposes a hierarchical correlation histogram analysis based on the grayscale distribution degree of pixel intensity by constructing a correlation histogram, that can improves the adaptive contrast enhancement for specific objects. The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy. The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values. Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.

Stratified mixture modeling for segmentation of white-matter lesions in brain MR images

Accurate characterization of white-matter lesions from magnetic resonance (MR) images has increasing importance for diagnosis and management of treatment of certain neurological diseases, and can be performed in an objective and effective way by automated lesion segmentation. This usually involves modeling the whole-brain MR intensity distribution, however, capturing various sources of MR intensity variability and lesion heterogeneity results in highly complex whole-brain MR intensity models, thus their robust estimation on a large set of MR images presents a huge challenge. We propose a novel approach employing stratified mixture modeling, where the main premise is that the otherwise complex whole-brain model can be reduced to a tractable parametric form in small brain subregions. We show on MR images of multiple sclerosis (MS) patients with different lesion loads that robust estimators enable accurate mixture modeling of MR intensity in small brain subregions even in the presence of lesions. Recombination of the mixture models across strata provided an accurate whole-brain MR intensity model. Increasing the number of subregions and, thereby, the model complexity, consistently improved the accuracy of whole-brain MR intensity modeling and segmentation of normal structures. The proposed approach was incorporated into three unsupervised lesion segmentation methods and, compared to original and three other state-of-the-art methods, the proposed modeling approach significantly improved lesion segmentation according to increased Dice similarity indices and lower number of false positives on real MR images of 30 patients with MS.

Accurate cortical tissue classification on MRI by modeling cortical folding patterns

Accurate tissue classification is a crucial prerequisite to MRI morphometry. Automated methods based on intensity histograms constructed from the entire volume are challenged by regional intensity variations due to local radiofrequency artifacts as well as disparities in tissue composition, laminar architecture and folding patterns. Current work proposes a novel anatomy-driven method in which parcels conforming cortical folding were regionally extracted from the brain. Each parcel is subsequently classified using nonparametric mean shift clustering. Evaluation was carried out on manually labeled images from two datasets acquired at 3.0 Tesla (n = 15) and 1.5 Tesla (n = 20). In both datasets, we observed high tissue classification accuracy of the proposed method (Dice index >97.6% at 3.0 Tesla, and >89.2% at 1.5 Tesla). Moreover, our method consistently outperformed state-of-the-art classification routines available in SPM8 and FSL-FAST, as well as a recently proposed local classifier that partitions the brain into cubes. Contour-based analyses localized more accurate white matter-gray matter (GM) interface classification of the proposed framework compared to the other algorithms, particularly in central and occipital cortices that generally display bright GM due to their highly degree of myelination. Excellent accuracy was maintained, even in the absence of correction for intensity inhomogeneity. The presented anatomy-driven local classification algorithm may significantly improve cortical boundary definition, with possible benefits for morphometric inference and biomarker discovery.

Quantitative evaluation of fMRI retinotopic maps, from V1 to V4, for cognitive experiments

FMRI retinotopic mapping is a non-invasive technique for the delineation of low-level visual areas in individual subjects. It generally relies upon the analysis of functional responses to periodic visual stimuli that encode eccentricity or polar angle in the visual field. This technique is used in vision research when the precise assignation of brain activation to retinotopic areas is an issue. It involves processing steps computed with different algorithms and embedded in various software suites. Manual intervention may be needed for some steps. Although the diversity of the available processing suites and manual interventions may potentially introduce some differences in the final delineation of visual areas, no documented comparison between maps obtained with different procedures has been reported in the literature. To explore the effect of the processing steps on the quality of the maps obtained, we used two tools, BALC, which relies on a fully automated procedure, and BrainVoyager, where areas are delineated “by hand” on the brain surface. To focus on the mapping procedures specifically, we used the same SPM pipeline for pretreatment and the same tissue segmentation tool. We document the consistency and differences of the fMRI retinotopic maps obtained from “routine retinotopy” experiments on 10 subjects. The maps obtained by skilled users are never fully identical. However, the agreement between the maps, around 80% for low-level areas, is probably sufficient for most applications. Our results also indicate that assigning cognitive activations, following a specific experiment (here, color perception), to individual retinotopic maps is not free of errors. We provide measurements of this error, that may help for the cautious interpretation of cognitive activation projection onto fMRI retinotopic maps. On average, the magnitude of the error is about 20%, with much larger differences in a few subjects. More variability may even be expected with less trained users or using different acquisition parameters and preprocessing chains.

Fully automated whole-head segmentation with improved smoothness and continuity, with theory reviewed

Individualized current-flow models are needed for precise targeting of brain structures using transcranial electrical or magnetic stimulation (TES/TMS). The same is true for current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). The first step in generating such models is to obtain an accurate segmentation of individual head anatomy, including not only brain but also cerebrospinal fluid (CSF), skull and soft tissues, with a field of view (FOV) that covers the whole head. Currently available automated segmentation tools only provide results for brain tissues, have a limited FOV, and do not guarantee continuity and smoothness of tissues, which is crucially important for accurate current-flow estimates. Here we present a tool that addresses these needs. It is based on a rigorous Bayesian inference framework that combines image intensity model, anatomical prior (atlas) and morphological constraints using Markov random fields (MRF). The method is evaluated on 20 simulated and 8 real head volumes acquired with magnetic resonance imaging (MRI) at 1 mm3 resolution. We find improved surface smoothness and continuity as compared to the segmentation algorithms currently implemented in Statistical Parametric Mapping (SPM). With this tool, accurate and morphologically correct modeling of the whole-head anatomy for individual subjects may now be feasible on a routine basis. Code and data are fully integrated into SPM software tool and are made publicly available. In addition, a review on the MRI segmentation using atlas and the MRF over the last 20 years is also provided, with the general mathematical framework clearly derived.

A unified Bayesian hierarchical model for MRI tissue classification

Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different steps, we extend a previous work to develop a unified Bayesian hierarchical model, which addresses both the partial volume effect and intensity non-uniformity, the two major acquisition artifacts, simultaneously. We adopted a normal mixture model with the means and variances depending on the tissue types of voxels to model the observed intensity values. We modeled the relationship among the components of the index vector of tissue types by a hidden Markov model, which captures the spatial similarity of voxels. Furthermore, we addressed the partial volume effect by construction of a higher resolution image in which each voxel is divided into subvoxels. Finally, We achieved the bias field correction by using a Gaussian Markov random field model with a band precision matrix designed in light of image filtering. Sparse matrix methods and parallel computations based on conditional independence are exploited to improve the speed of the Markov chain Monte Carlo simulation. The unified model provides more accurate tissue classification results for both simulated and real data sets.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

Liver segmentation based on Snakes Model and improved GrowCut algorithm in abdominal CT image

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

Retinotopic and lateralized processing of spatial frequencies in human visual cortex during scene categorization

Using large natural scenes filtered in spatial frequencies, we aimed to demonstrate that spatial frequency processing could not only be retinotopically mapped but could also be lateralized in both hemispheres. For this purpose, participants performed a categorization task using large black and white photographs of natural scenes (indoors vs. outdoors, with a visual angle of 24° × 18°) filtered in low spatial frequencies (LSF), high spatial frequencies (HSF), and nonfiltered scenes, in block-designed fMRI recording sessions. At the group level, the comparison between the spatial frequency content of scenes revealed first that, compared with HSF, LSF scene categorization elicited activation in the anterior half of the calcarine fissures linked to the peripheral visual field, whereas, compared with LSF, HSF scene categorization elicited activation in the posterior part of the occipital lobes, which are linked to the fovea, according to the retinotopic property of visual areas. At the individual level, functional activations projected on retinotopic maps revealed that LSF processing was mapped in the anterior part of V1, whereas HSF processing was mapped in the posterior and ventral part of V2, V3, and V4. Moreover, at the group level, direct interhemispheric comparisons performed on the same fMRI data highlighted a right-sided occipito-temporal predominance for LSF processing and a left-sided temporal cortex predominance for HSF processing, in accordance with hemispheric specialization theories. By using suitable method of analysis on the same data, our results enabled us to demonstrate for the first time that spatial frequencies processing is mapped retinotopically and lateralized in human occipital cortex.

Class-specific weighting for Markov random field estimation: application to medical image segmentation

Many estimation tasks require Bayesian classifiers capable of adjusting their performance (e.g. sensitivity/specificity). In situations where the optimal classification decision can be identified by an exhaustive search over all possible classes, means for adjusting classifier performance, such as probability thresholding or weighting the a posteriori probabilities, are well established. Unfortunately, analogous methods compatible with Markov random fields (i.e. large collections of dependent random variables) are noticeably absent from the literature. Consequently, most Markov random field (MRF) based classification systems typically restrict their performance to a single, static operating point (i.e. a paired sensitivity/specificity). To address this deficiency, we previously introduced an extension of maximum posterior marginals (MPM) estimation that allows certain classes to be weighted more heavily than others, thus providing a means for varying classifier performance. However, this extension is not appropriate for the more popular maximum a posteriori (MAP) estimation. Thus, a strategy for varying the performance of MAP estimators is still needed. Such a strategy is essential for several reasons: (1) the MAP cost function may be more appropriate in certain classification tasks than the MPM cost function, (2) the literature provides a surfeit of MAP estimation implementations, several of which are considerably faster than the typical Markov Chain Monte Carlo methods used for MPM, and (3) MAP estimation is used far more often than MPM. Consequently, in this paper we introduce multiplicative weighted MAP (MWMAP) estimation-achieved via the incorporation of multiplicative weights into the MAP cost function-which allows certain classes to be preferred over others. This creates a natural bias for specific classes, and consequently a means for adjusting classifier performance. Similarly, we show how this multiplicative weighting strategy can be applied to the MPM cost function (in place of the strategy we presented previously), yielding multiplicative weighted MPM (MWMPM) estimation. Furthermore, we describe how MWMAP and MWMPM can be implemented using adaptations of current estimation strategies such as iterated conditional modes and MPM Monte Carlo. To illustrate these implementations, we first integrate them into two separate MRF-based classification systems for detecting carcinoma of the prostate (CaP) on (1) digitized histological sections from radical prostatectomies and (2) T2-weighted 4 Tesla ex vivo prostate MRI. To highlight the extensibility of MWMAP and MWMPM to estimation tasks involving more than two classes, we also incorporate these estimation criteria into a MRF-based classifier used to segment synthetic brain MR images. In the context of these tasks, we show how our novel estimation criteria can be used to arbitrarily adjust the sensitivities of these systems, yielding receiver operator characteristic curves (and surfaces).

Joint probabilistic model of shape and intensity for multiple abdominal organ segmentation from volumetric CT images

We propose a novel joint probabilistic model that correlates a new probabilistic shape model with the corresponding global intensity distribution to segment multiple abdominal organs simultaneously. Our probabilistic shape model estimates the probability of an individual voxel belonging to the estimated shape of the object. The probability density of the estimated shape is derived from a combination of the shape variations of target class and the observed shape information. To better capture the shape variations, we used probabilistic principle component analysis optimized by expectation maximization to capture the shape variations and reduce computational complexity. The maximum a posteriori estimation was optimized by the iterated conditional mode-expectation maximization. We used 72 training datasets including low- and high-contrast CT images to construct the shape models for the liver, spleen and both kidneys. We evaluated our algorithm on 40 test datasets that were grouped into normal (34 normal cases) and pathologic (6 datasets) classes. The testing datasets were from different databases and manual segmentation was performed by different clinicians. We measured the volumetric overlap percentage error, relative volume difference, average square symmetric surface distance, false positive rate and false negative rate and our method achieved accurate and robust segmentation for multiple abdominal organs simultaneously.

An open source multivariate framework for n-tissue segmentation with evaluation on public data

We introduce Atropos, an ITK-based multivariate n-class open source segmentation algorithm distributed with ANTs ( http://www.picsl.upenn.edu/ANTs). The Bayesian formulation of the segmentation problem is solved using the Expectation Maximization (EM) algorithm with the modeling of the class intensities based on either parametric or non-parametric finite mixtures. Atropos is capable of incorporating spatial prior probability maps (sparse), prior label maps and/or Markov Random Field (MRF) modeling. Atropos has also been efficiently implemented to handle large quantities of possible labelings (in the experimental section, we use up to 69 classes) with a minimal memory footprint. This work describes the technical and implementation aspects of Atropos and evaluates its performance on two different ground-truth datasets. First, we use the BrainWeb dataset from Montreal Neurological Institute to evaluate three-tissue segmentation performance via (1) K-means segmentation without use of template data; (2) MRF segmentation with initialization by prior probability maps derived from a group template; (3) Prior-based segmentation with use of spatial prior probability maps derived from a group template. We also evaluate Atropos performance by using spatial priors to drive a 69-class EM segmentation problem derived from the Hammers atlas from University College London. These evaluation studies, combined with illustrative examples that exercise Atropos options, demonstrate both performance and wide applicability of this new platform-independent open source segmentation tool.

Segmentation of skin lesions in 2-D and 3-D ultrasound images using a spatially coherent generalized Rayleigh mixture model

This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images.

Topology-based nonlocal fuzzy segmentation of brain MR image with inhomogeneous and partial volume intensity

PURPOSE

The aim was to automatically segment brain magnetic resonance (MR) image with inhomogeneous and partial volume (PV) intensity for brain and neurophysiology analysis.

METHODS

Rather than assuming the presence of a single bias field over the image data, we first apply a local model to MR image analysis. With the brain topology knowledge, several specific local regions are selected, and typical brain tissues are then extracted for the prior estimation of fuzzy clustering center and member function. A new nonlocal fuzzy labeling scheme is applied to global optimization segmentation based on the block comparison and distance weight, which is robust to noise and inhomogeneous intensity. The nonlocal labeling provides optimized fuzzy member value and local intensity estimation of brain tissues such as cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM). In addition to inhomogeneous intensity, PV may lead to error segmentation. To correct error segmentation because of PV, this article also provides two correction schemes. The first one is to extract CSF in deep sulci, which captures more CSF candidate by intensity comparison and topology shape comparison. The local pure CSF, WM, and GM is then estimated to correct the interfaces of CSF/GM and WM/GM.

RESULTS

The segmentation experiments are performed on both brainweb-simulated images and Internet brain segmentation repository database (IBSR) real images. The experimental results demonstrate the robust and efficient performance of our approach.

CONCLUSIONS

Our approach can be applied to automatic segmentation of the brain MR image.

A BOLD signature of eyeblinks in the visual cortex

We are usually unaware of the brief but large illumination changes caused by blinks, presumably because of blink suppression mechanisms. In fMRI however, increase of the BOLD signal was reported in the visual cortex, e.g. during blocks of voluntary blinks (Bristow, Frith and Rees, 2005) or after spontaneous blinks recorded during the prolonged fixation of a static stimulus (Tse, Baumgartner and Greenlee, 2010). We tested whether such activation, possibly related to illumination changes, was also present during standard fMRI retinotopic and visual experiments and was large enough to contaminate the BOLD signal we are interested in. We monitored in a 3T scanner the eyeblinks of 14 subjects who observed three different types of visual stimuli, including periodic rotating wedges and contracting/expanding rings, event-related Mondrians and graphemes, while fixating. We performed event-related analyses on the set of detected spontaneous blinks. We observed large and widespread BOLD responses related to blinks in the visual cortex of every subject and whatever the visual stimulus. The magnitude of the modulation was comparable to visual stimulation. However, blink-related activations lay mostly in the anterior parts of retinotopic visual areas, coding the periphery of the visual field well beyond the extent of our stimuli. Blinks therefore represent an important source of BOLD variations in the visual cortex and a troublesome source of noise since any correlation, even weak, between the distribution of blinks and a tested protocol could trigger artifactual activities. However, the typical signature of blinks along the anterior calcarine and the parieto-occipital sulcus allows identifying, even in the absence of eyetracking, fMRI protocols possibly contaminated by a heterogeneous distribution of blinks.

Spatial based expectation maximizing (EM)

BACKGROUND

Expectation maximizing (EM) is one of the common approaches for image segmentation.

METHODS

an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.

RESULTS

the findings show that the proposed algorithm produces higher similarity index.

CONCLUSIONS

experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.

Weighted maximum posterior marginals for random fields using an ensemble of conditional densities from multiple Markov chain Monte Carlo simulations

The ability of classification systems to adjust their performance (sensitivity/specificity) is essential for tasks in which certain errors are more significant than others. For example, mislabeling cancerous lesions as benign is typically more detrimental than mislabeling benign lesions as cancerous. Unfortunately, methods for modifying the performance of Markov random field (MRF) based classifiers are noticeably absent from the literature, and thus most such systems restrict their performance to a single, static operating point (a paired sensitivity/specificity). To address this deficiency we present weighted maximum posterior marginals (WMPM) estimation, an extension of maximum posterior marginals (MPM) estimation. Whereas the MPM cost function penalizes each error equally, the WMPM cost function allows misclassifications associated with certain classes to be weighted more heavily than others. This creates a preference for specific classes, and consequently a means for adjusting classifier performance. Realizing WMPM estimation (like MPM estimation) requires estimates of the posterior marginal distributions. The most prevalent means for estimating these--proposed by Marroquin--utilizes a Markov chain Monte Carlo (MCMC) method. Though Marroquin's method (M-MCMC) yields estimates that are sufficiently accurate for MPM estimation, they are inadequate for WMPM. To more accurately estimate the posterior marginals we present an equally simple, but more effective extension of the MCMC method (E-MCMC). Assuming an identical number of iterations, E-MCMC as compared to M-MCMC yields estimates with higher fidelity, thereby 1) allowing a far greater number and diversity of operating points and 2) improving overall classifier performance. To illustrate the utility of WMPM and compare the efficacies of M-MCMC and E-MCMC, we integrate them into our MRF-based classification system for detecting cancerous glands in (whole-mount or quarter) histological sections of the prostate.