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.     

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.     

Fully automated segmentation and morphometrical analysis of muscle fiber images.

BACKGROUND: Measurement of muscle fiber size and determination of size distribution is important in the assessment of neuromuscular disease. Fiber size estimation by simple inspection is inaccurate and subjective. Manual segmentation and measurement are time-consuming and tedious. We therefore propose an automated image analysis method for objective, reproducible, and time-saving measurement of muscle fibers in routinely hematoxylin-eosin stained cryostat sections. METHODS: The proposed segmentation technique makes use of recent advances in level set based segmentation, where classical edge based active contours are extended by region based cues, such as color and texture. Segmentation and measurement are performed fully automatically. Multiple morphometric parameters, i.e., cross sectional area, lesser diameter, and perimeter are assessed in a single pass. The performance of the computed method was compared to results obtained by manual measurement by experts. RESULTS: The correct classification rate of the computed method was high (98%). Segmentation and measurement results obtained manually or automatically did not reveal any significant differences. CONCLUSIONS: The presented region based active contour approach has been proven to accurately segment and measure muscle fibers. Complete automation minimizes user interaction, thus, batch processing, as well as objective and reproducible muscle fiber morphometry are provided.     

Microbleed detection using automated segmentation (MIDAS): a new method applicable to standard clinical MR images

Background

Cerebral microbleeds, visible on gradient-recalled echo (GRE) T2* MRI, have generated increasing interest as an imaging marker of small vessel diseases, with relevance for intracerebral bleeding risk or brain dysfunction.

Methodology/Principal Findings

Manual rating methods have limited reliability and are time-consuming. We developed a new method for microbleed detection using automated segmentation (MIDAS) and compared it with a validated visual rating system. In thirty consecutive stroke service patients, standard GRE T2* images were acquired and manually rated for microbleeds by a trained observer. After spatially normalizing each patient''s GRE T2* images into a standard stereotaxic space, the automated microbleed detection algorithm (MIDAS) identified cerebral microbleeds by explicitly incorporating an “extra” tissue class for abnormal voxels within a unified segmentation-normalization model. The agreement between manual and automated methods was assessed using the intraclass correlation coefficient (ICC) and Kappa statistic. We found that MIDAS had generally moderate to good agreement with the manual reference method for the presence of lobar microbleeds (Kappa = 0.43, improved to 0.65 after manual exclusion of obvious artefacts). Agreement for the number of microbleeds was very good for lobar regions: (ICC = 0.71, improved to ICC = 0.87). MIDAS successfully detected all patients with multiple (≥2) lobar microbleeds.

Conclusions/Significance

MIDAS can identify microbleeds on standard MR datasets, and with an additional rapid editing step shows good agreement with a validated visual rating system. MIDAS may be useful in screening for multiple lobar microbleeds.     

Feature-based,automated segmentation of cerebral infarct patterns using T2- and diffusion-weighted imaging

Diffusion-weighted imaging enables the diagnosis of cerebral ischemias very early, thus supporting therapies such as thrombolysis. However, morphology and tissue-characterizing parameters (e.g. relaxation times or water diffusion) may vary strongly in ischemic regions, indicating different underlying pathologic processes. As the determination of the parameters by a supervised segmentation is very time consuming, we evaluated whether different infarct patterns may be segmented by an automated, multidimensional feature-based method using a unified segmentation procedure. Ischemias were classified into 5 characteristic patterns. For each class, a 3D histogram based on T(2)- and diffusion-weighted images as well as calculated apparent diffusion coefficients (ADC) was generated from a representative data set. Healthy and pathologic tissue classes were segmented in the histogram as separate, local density maxima with freely shaped borders. Segmentation control parameters were optimized in a 3-step procedure. The method was evaluated using synthetic images as well as results of a supervised segmentation. For the analysis of cerebral ischemias, the optimal control parameter set led to sensitivities and specificities between 1.0 and 0.9.     

Rapid three-dimensional segmentation of the carotid bifurcation from serial MR images

The current trend in computational hemodynamics is to employ realistic models derived from ex vivo or in vivo imaging. Such studies typically produce a series of images from which the lumen boundaries must first be individually extracted (i.e., two-dimensional segmentation), and then serially reconstructed to produce the three-dimensional lumen surface geometry. In this paper, we present a rapid three-dimensional segmentation technique that combines these two steps, based on the idea of an expanding virtual balloon. This three-dimensional technique is demonstrated in application to finite element meshing and CFD modeling of flow in the carotid bifurcation of a normal volunteer imaged with black blood MRI. Wall shear stress patterns computed using a mesh generated with the three-dimensional technique agree well with those computed using a mesh generated from conventional two-dimensional segmentation and serial reconstruction. In addition to reducing the time required to extract the lumen surface from hours to minutes, our approach is easy to learn and use and requires minimal user intervention, which can potentially increase the accuracy and precision of quantitative and longitudinal studies of hemodynamics and vascular disease.     

Biochemical characterization of human brain and kidney tissues by magnetic resonance spectroscopy

In vivo and in vitro Magnetic Resonance Spectroscopy is useful for monitoring changes in intracellular metabolites of human cerebral and renal tissues. Healthy and tumoral tissues of different histologic types have been characterized from a biochemical point of view. In vitro molecular characterization is performed on both the aqueous and lipid extracts of surgically removed tissue biopsies, after in vivo MRS, yielding a full picture of tissue biochemistry. Biochemical markers of healthy brain and kidney and of their relative neoplastic lesions have been disclosed. Moreover, some biochemical features can differentiate neoplasm within the same histological type. Ex vivo MRS also gives molecular information related to necrotic phenomena in glial tumors. MRS finding paralleled histologic data and new knowledge about the molecular base of proliferative neoplastic phenomena can be obtained.     

Elastomechanical characterization of brain tissues.

The fluid-induced changes in the intracranial pressure which have important clinical implications are believed to be largely determined by the elastomechanical properties of the brain tissues. To define and evaluate the elastomechanical characteristics of the brain tissues a nonlinear hyperelastic hollow spherical shell has been employed to model the craniospinal complex for its fluid-induced intracranial pressure volume changes. The strain energy function proposed by Hart-Smith has been used to derive the constitutive equations. In 10 dogs, fluid has been infused in the lateral ventricle of the brain. The resulting changes in the ventricular fluid pressure (VFP) and the epidural pressure (EDP) have been recorded. The plot of pressure as a function of volume increases first, reaches a maximum, decreases, reaches a minimum and increases monotonously. The values of maximum and minimum pressures (pv max and pv min) due to fluid infusion are found to be, respectively, 42.4 +/- 15.4 mmHg and 33.1 +/- 12.2 mmHg. The pressure achieved the maximum and minimum values with infusion of 0.19 +/- 0.09 ml and 0.51 +/- 0.15 ml of fluid, respectively. The elastomechanical parameters of the Hart-Smith function that characterize the brain tissues have been evaluated by matching the experimentally obtained pressure-volume curves with the corresponding model generated curves. It is found that the agreement between the experimentally obtained pressure-volume curves and the corresponding Hart-Smith profile is satisfactory at a high inflation level but less so at the lower inflation level.     

LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks

Digital pathology and microscope image analysis is widely used in comprehensive studies of cell morphology. Identification and analysis of leukocytes in blood smear images, acquired from bright field microscope, are vital for diagnosing many diseases such as hepatitis, leukaemia and acquired immune deficiency syndrome (AIDS). The major challenge for robust and accurate identification and segmentation of leukocyte in blood smear images lays in the large variations of cell appearance such as size, colour and shape of cells, the adhesion between leukocytes (white blood cells, WBCs) and erythrocytes (red blood cells, RBCs), and the emergence of substantial dyeing impurities in blood smear images. In this paper, an end‐to‐end leukocyte localization and segmentation method is proposed, named LeukocyteMask, in which pixel‐level prior information is utilized for supervisor training of a deep convolutional neural network, which is then employed to locate the region of interests (ROI) of leukocyte, and finally segmentation mask of leukocyte is obtained based on the extracted ROI by forward propagation of the network. Experimental results validate the effectiveness of the propose method and both the quantitative and qualitative comparisons with existing methods indicate that LeukocyteMask achieves a state‐of‐the‐art performance for the segmentation of leukocyte in terms of robustness and accuracy .     

Quantification of meniscal volume by segmentation of 3T magnetic resonance images

Meniscal injuries place the knee at risk for early osteoarthritis (OA) because they disrupt their load-bearing capabilities. Partial resection is routinely performed to alleviate symptomatic meniscal tears. While the removal of meniscal tissue may not be the only factor associated with partial meniscectomy outcome, the amount removed certainly contributes to functional loss. It is unknown, however, whether there is a critical amount of meniscal tissue that can be removed without diminishing the structure's chondroprotective role. In order to examine the existence of such a threshold, it is necessary to accurately quantify meniscal volume both before and after partial meniscectomy to determine the amount of meniscal tissue removed. Therefore, our goal was to develop and validate an MR-based method for assessing meniscal volume. The specific aims were: (1) to evaluate the feasibility of the MR-based segmentation method; (2) to determine the method's reliability for repeated measurements; and (3) to validate its accuracy in situ. MR images were obtained on a 3T magnet, and each scan was segmented using a biplanar approach. The MR-based volumes for each specimen were compared to those measured by water displacement. The results indicate that the biplanar approach of measuring meniscal volumes is accurate and reliable. The calculated volumes of the menisci were within 5% of the true values, the coefficients of variation were 4%, and the intraclass correlation coefficients were greater than 0.96. These data demonstrate that this method could be used to measure the amount of meniscal tissue excised during partial meniscectomy to within 125.7 mm(3).     

Probabilistic segmentation and intensity estimation for microarray images

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common 'doughnut-shaped' spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.     

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PurposeIn this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.MethodsOur method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.ResultsWe tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.ConclusionThe proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.     

Mechanical properties of soft human tissues under dynamic loading

The dynamic response of soft human tissues in hydrostatic compression and simple shear is studied using the Kolsky bar technique. We have made modifications to the technique that allow loading of a soft tissue specimen in hydrostatic compression or simple shear. The dynamic response of human tissues (from stomach, heart, liver, and lung of cadavers) is obtained, and analyzed to provide measures of dynamic bulk modulus and shear response for each tissue type. The dynamic bulk response of these tissues is easily described by a linear fit for the bulk modulus in this pressure range, whereas the dynamic shearing response of these tissues is strongly non-linear, showing a near exponential growth of the shear stress.     

A framework for the automated analysis of subcellular patterns in human protein atlas images

The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a protein's function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.     

Topology-based fluorescence image analysis for automated cell identification and segmentation

Cell segmentation refers to the body of techniques used to identify cells in images and extract biologically relevant information from them; however, manual segmentation is laborious and subjective. We present Topological Boundary Line Estimation using Recurrence Of Neighbouring Emissions (TOBLERONE), a topological image analysis tool which identifies persistent homological image features as opposed to the geometric analysis commonly employed. We demonstrate that topological data analysis can provide accurate segmentation of arbitrarily-shaped cells, offering a means for automatic and objective data extraction. One cellular feature of particular interest in biology is the plasma membrane, which has been shown to present varying degrees of lipid packing, or membrane order, depending on the function and morphology of the cell type. With the use of environmentally-sensitive dyes, images derived from confocal microscopy can be used to quantify the degree of membrane order. We demonstrate that TOBLERONE is capable of automating this task.     

A sub-domain inverse finite element characterization of hyperelastic membranes including soft tissues

Quantification of the mechanical behavior of hyperelastic membranes in their service configuration, particularly biological tissues, is often challenging because of the complicated geometry, material heterogeneity, and nonlinear behavior under finite strains. Parameter estimation thus requires sophisticated techniques like the inverse finite element method. These techniques can also become difficult to apply, however, if the domain and boundary conditions are complex (e.g. a non-axisymmetric aneurysm). Quantification can alternatively be achieved by applying the inverse finite element method over sub-domains rather than the entire domain. The advantage of this technique, which is consistent with standard experimental practice, is that one can assume homogeneity of the material behavior as well as of the local stress and strain fields. In this paper, we develop a sub-domain inverse finite element method for characterizing the material properties of inflated hyperelastic membranes, including soft tissues. We illustrate the performance of this method for three different classes of materials: neo-Hookean, Mooney Rivlin, and Fung-exponential.     

An automatic segmentation and classification framework for anti-nuclear antibody images

Autoimmune disease is a disorder of immune system due to the over-reaction of lymphocytes against one's own body tissues. Anti-Nuclear Antibody (ANA) is an autoantibody produced by the immune system directed against the self body tissues or cells, which plays an important role in the diagnosis of autoimmune diseases. Indirect ImmunoFluorescence (IIF) method with HEp-2 cells provides the major screening method to detect ANA for the diagnosis of autoimmune diseases. Fluorescence patterns at present are usually examined laboriously by experienced physicians through manually inspecting the slides with the help of a microscope, which usually suffers from inter-observer variability that limits its reproducibility. Previous researches only provided simple segmentation methods and criterions for cell segmentation and recognition, but a fully automatic framework for the segmentation and recognition of HEp-2 cells had never been reported before. This study proposes a method based on the watershed algorithm to automatically detect the HEp-2 cells with different patterns. The experimental results show that the segmentation performance of the proposed method is satisfactory when evaluated with percent volume overlap (PVO: 89%). The classification performance using a SVM classifier designed based on the features calculated from the segmented cells achieves an average accuracy of 96.90%, which outperforms other methods presented in previous studies. The proposed method can be used to develop a computer-aided system to assist the physicians in the diagnosis of auto-immune diseases.     

Immunochemical characterization of aldo-keto reductases from human tissues

Aldose reductase, aldehyde reductase and carbonyl reductase constitute a family of monomeric NADPH-dependent oxidoreductases with similar physical and chemical properties. Characterization of the enzymes from human tissues by immunotitration and an enzyme immunoassay indicated that, despite their apparent likeness, the three reductases do not cross-react immunochemically.