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Results for “Automated image segmentation”

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PubMed2006

Automated segmentation of lateral ventricles from human and primate magnetic resonance images using cognition network technology.

Automatic segmentation of different types of tissue from magnetic resonance images is of great importance for clinical and research applications, particularly large-scale and longitudinal studies of brain pathology. We developed a fully automated algorithm for the segmentation of lateral ventricles from cranial magnetic resonance images. This problem is of interest in the study of schizophrenia, dementia and other neuropsychiatric disorders. Our algorithm achieves comparable results to expert human raters. The theoretical approach, which is based on an emerging object-oriented technology that has been adapted and evaluated to process 3D data for the first time, may, in the future, be transferred to other important problems of magnetic resonance image analysis like gray/white matter segmentation.

Algorithms↗

PubMed1993

Standardization and quality control of quantitative microscopy in pathology.

Standardization and quality control of quantitative microscopy techniques are distinct but related concerns. The first deals with the great variety of quantitative methods, measured features, and even response variables used in investigation of biological or clinical processes. The latter deals with reproducibility of results from those investigations across time and test performance sites. Though distinct, efforts for standardization and quality control are inherently interactive. Consensus on standard methods, instrumentation, and data analysis is hard to achieve in fields developing as rapidly as quantitative microscopy. Consensus is possible, however, on the issues that affect test performance and interpretation. For example, issues of specimen type, fixation, processing, and staining affect image cytometry just as they do flow cytometry. Raw data acquisition issues include area sampling rules and fidelity of optical and sensor systems (light wavelength, glare/stray light, lens aberrations, numerical aperture, depth of focus, scan precision, pixel spacing and depth, sensor linearity, and stability). Intermediate data issues are primarily related to image foreground/background segmentation techniques--automated versus manual, object-specific versus field-based. Data reduction and interpretation procedures also provide many roads for divergence from uniformity. Each of these issues must be considered in terms of its effect on comparability and utility of quantitative microscopy results. Quality control for quantitative microscopy is as important as standardization for its use in research programs and with clinical specimens. The sine qua non of quality control is comparison of experimental results against a known "correct" value to estimate accuracy, and against other experimental results to estimate precision.(ABSTRACT TRUNCATED AT 250 WORDS)

Colonic Neoplasms↗

PubMed2000

Medical image segmentation using analysis of isolable-contour maps.

A common challenge for automated segmentation techniques is differentiation between images of close objects that have similar intensities, whose boundaries are often blurred due to partial-volume effects. We propose a novel approach to segmentation of two-dimensional images, which addresses this challenge. Our method, which we call intrinsic shape for segmentation (ISeg), analyzes isolabel-contour maps to identify coherent regions that correspond to major objects. ISeg generates an isolabel-contour map for an image by multilevel thresholding with a fine partition of the intensity range. ISeg detects object boundaries by comparing the shape of neighboring isolabel contours from the map. ISeg requires only little effort from users; it does not require construction of shape models of target objects. In a formal validation with computed-tomography angiography data, we showed that ISeg was more robust than conventional thresholding, and that ISeg's results were comparable to results of manual tracing.

Algorithms↗

PubMed2006

Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by X-ray angiography and intravascular ultrasound.

The relationships among vascular geometry, hemodynamics, and plaque development in the coronary arteries are complex and not yet well understood. This paper reports a methodology for the quantitative analysis of in vivo coronary morphology and hemodynamics, with particular emphasis placed on the critical issues of image segmentation and the automated classification of disease severity. We were motivated by the observation that plaque more often developed at the inner curvature of a vessel, presumably due to the relatively lower wall shear stress at these locations. The presented studies are based on our validated methodology for the three-dimensional fusion of intravascular ultrasound (IVUS) and X-ray angiography, introducing a novel approach for IVUS segmentation that incorporates a robust, knowledge-based cost function and a fully optimal, three-dimensional segmentation algorithm. Our first study shows that circumferential plaque distribution depends on local vessel curvature in the majority of vessels. The second study analyzes the correlation between plaque distribution and wall shear stress in a set of 48 in vivo vessel segments. The results were conclusive for both studies, with a stronger correlation of circumferential plaque thickness with local curvature than with wall shear stress. The inverse relationship between local wall shear stress and plaque thickness was significantly more pronounced (p<0.025) in vessel cross sections exhibiting compensatory enlargement (positive remodeling) without luminal narrowing than when the full spectrum of disease severity was considered. The inverse relationship was no longer observed in vessels where less than 35% of vessel cross sections remained without luminal narrowing. The findings of this study confirm, in vivo, the hypothesis that relatively lower wall shear stress is associated with early plaque development.

Computer Simulation↗

PubMed1995

Live cell image segmentation.

A major requirement of an automated, real-time, computer vision-based cell tracking system is an efficient method for segmenting cell images. The usual segmentation algorithms proposed in the literature exhibit weak performance on live unstained cell images, which can be characterized as being of low contrast, intensity-variant, and unevenly illuminated. We propose a two-stage segmentation strategy which involves: 1) extracting an approximate region containing the cell and part of the background near the cell, and 2) segmenting the cell from the background within this region. The approach effectively reduces the influence of peripheral background intensities and texture on the extraction of a cell region. The experimental results show that this approach for segmenting cell images is both fast and robust.

Algorithms↗

PubMed2005

Three-dimensional reconstruction of tumor microvasculature: simultaneous visualization of multiple components in paraffin-embedded tissue.

Three-dimensional (3D) visualization of microscopic structures may provide useful information about the exact 3D configuration, and offers a useful tool to examine the spatial relationship between different components in tissues. A promising field for 3D investigation is the microvascular architecture in normal and pathological tissue, especially because pathological angiogenesis plays a key role in tumor growth and metastasis formation. This paper describes an improved method for 3D reconstruction of microvessels and other microscopic structures in transmitted light microscopy. Serial tissue sections were stained for the endothelial marker CD34 to highlight microvessels and corresponding images were selected and aligned. Alignment of stored images was further improved by automated non-rigid image registration, and automated segmentation of microvessels was performed. Using this technique, 3D reconstructions were produced of the vasculature of the normal brain. Also, to illustrate the complexity of tumor vasculature, 3D reconstructions of two brain tumors were performed: a hemangioblastoma and a glioblastoma multiforme. The possibility of multiple component visualization was shown in a 3D reconstruction of endothelium and pericytes of normal cerebellar cortex and a hemangioblastoma using alternate staining for CD34 and alpha-smooth muscle actin in serial sections, and of a GBM using immunohistochemical double staining. In conclusion, the described 3D reconstruction procedure provides a promising tool for simultaneous visualization of microscopic structures.

Brain Neoplasms↗

PubMed2006

Semi-automated CT segmentation using optic flow and Fourier interpolation techniques.

In radiotherapy treatment planning, tumor volumes and anatomical structures are manually contoured for dose calculation, which takes time for clinicians. This study examines the use of semi-automated segmentation of CT images. A few high curvature points are manually drawn on a CT slice. Then Fourier interpolation is used to complete the contour. Consequently, optical flow, a deformable image registration method, is used to map the original contour to other slices. This technique has been applied successfully to contour anatomical structures and tumors. The maximum difference between the mapped contours and manually drawn contours was 6 pixels, which is similar in magnitude to difference one would see in manually drawn contours by different clinicians. The technique fails when the region to contour is topologically different between two slices. A solution is recommended to manually delineate contours on a sparse subset of slices and then map in both directions to fill the remaining slices.

Algorithms↗

PubMed1999

Age-associated changes in rhesus CNS composition identified by MRI.

Multispectral automated segmentation of MR images of the brains of 10 young (5-8 years), 10 middle-aged (12-17 years), and 11 old (21-27 years) female rhesus monkeys revealed age-associated changes in brain volume and composition. Total brain parenchymal volume (expressed as fraction of intracranial volume-%ICV) decreased at a linear rate of 0.3+/-0.04% ICV/year. Up to age approximately 15 years, this loss was almost entirely due to gray matter loss, with a compensatory increase in cerebrospinal fluid (CSF), and possibly some white matter. Brain tissue composition, expressed as the gray matter/white matter volume ratio confirmed that gray matter loss exceeded white matter loss, but the rate of decline in the gray/white ratio began to slow after approximately 15 years. Comparison of these age-associated changes in rhesus brain with those in humans suggest that the brain aging in rhesus is a good model of human brain aging, but occurs approximately 3-fold faster.

Aging↗

PubMed1995

Automated screening for cytomegalovirus infected cells using image analysis. Comparison of two immunoenzymatic staining methods with respect to colour segmentation.

The detection of human cytomegalovirus (HCMV) infected poly-morphonuclear leukocytes (PMNLs) for early finding of the pp65 antigen using automated image analysis has been improved. The routinely used immunoenzyme peroxidase (PO) labelling has been replaced by alkaline phosphatase (AP) using new fuchsin as substrate. The number of automatically detected false positive objects due to incomplete inactivation of endogenous peroxidase and strong variations in counterstain intensity could be reduced by 81% using this AP staining method. The number of detected truly positive cells with both staining methods was not significantly different. Furthermore, a new image analysis system providing processing of colour images was evaluated. Since plain differences in absorption wavelength are required for colour segmentation, the red immuno-staining was combined with a green counterstain using methyl green. Screening of AP- instead of PO-stained slides in combination with colour segmentation resulted in a further reduction of the number of falsely detected alarms from 61% to 11%. Consequently, the sensitivity of the automated detection was improved. For AP staining detection of cells in frequencies of approximately one to one million was demonstrated. Screening for CMV-positive, alkaline phosphatase labelled cells using an image analysis system with colour segmentation resulted in a reduced false alarm rate, a better visual interpretation of the images and subsequently an increase in the sensitivity of the automated screening process.

Cytomegalovirus↗

PubMed2006

Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images.

This paper describes a fully automated segmentation and recognition scheme, which is designed to recognize lung anatomical structures in the human chest by segmenting the different chest internal organ and tissue regions sequentially from high-resolution chest CT images. A sequential region-splitting process is used to segment lungs, airway of bronchus, lung lobes and fissures based on the anatomical structures and statistical intensity distributions in CT images. The performance of our scheme is evaluated by segmenting lung structures from high-resolution multi-slice chest CT images from 44 patients; the validity of our method was proved by preliminary experimental results.

Humans↗

PubMed2006

Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning.

The purpose of this study was to design the steps necessary to create a tumor volume outline from the results of two automated multispectral magnetic resonance imaging segmentation methods and integrate these contours into radiation therapy treatment planning. Algorithms were developed to create a closed, smooth contour that encompassed the tumor pixels resulting from two automated segmentation methods: k-nearest neighbors and knowledge guided. These included an automatic three-dimensional (3D) expansion of the results to compensate for their undersegmentation and match the extended contouring technique used in practice by radiation oncologists. Each resulting radiation treatment plan generated from the automated segmentation and from the outlining by two radiation oncologists for 11 brain tumor patients was compared against the volume and treatment plan from an expert radiation oncologist who served as the control. As part of this analysis, a quantitative and qualitative evaluation mechanism was developed to aid in this comparison. It was found that the expert physician reference volume was irradiated within the same level of conformity when using the plans generated from the contours of the segmentation methods. In addition, any uncertainty in the identification of the actual gross tumor volume by the segmentation methods, as identified by previous research into this area, had small effects when used to generate 3D radiation therapy treatment planning due to the averaging process in the generation of margins used in defining a planning target volume.

Algorithms↗

PubMed1998

Breast cancer risk and measured mammographic density.

It has been well established that there is a positive correlation between the dense appearance of breast stroma and parenchyma on a mammogram and the risk of breast cancer. Subjective assessment by radiologists indicated relative risks on the order of 4 to 6 for the group of women whose mammograms showed a density of over 75% or more of the projected area compared to those with an absence of density. In order to obtain a more quantitative, continuous and reproducible means of estimating breast density, which is sensitive to small changes, we have developed quantitative methods for the analysis of mammographic density, which can be applied to digitized mammograms. These techniques have been validated in a nested case-control study on 708 women aged 40-59 years (on entry) who participated in a national mammographic screening study. An interactive image segmentation method and two completely automated techniques based on image texture and grey scale histogram measures have been developed and evaluated. While our methods all show statistically significant risk factors for dense breasts, the interactive method currently provides the highest risk values (relative risk 4.0, 95% confidence interval (CI) = 2.12-7.56) compared to a measure based on the shape of the image histogram (relative risk 3.35, 95% CI = 1.57-7.12) or the fractal dimension of the mammogram (relative risk 2.54, 95% CI = 1.14-5.68). All methods were highly consistent between images of the left and right breast and between the two standard views (cranio-caudal and medio-lateral oblique) of each breast, so that studies can be done by sampling only one of the four views per examination. There is a large number of factors in addition to breast density which affect the appearance of the mammogram. In particular, the assessment of density is made difficult where the breast is not uniformly compressed, e.g. at the periphery. We have designed and are currently evaluating an image processing algorithm that effectively corrects for this problem and have considered methods for controlling some of the variables of image acquisition in prospective studies. Measurements of breast density may be helpful in assigning risk groups to women. Such measurements might guide the frequency of mammographic screening, aid the study of breast cancer aetiology, and be useful in monitoring possible risk-modifying interventions. Using our techniques, we have been able to show that reduction of the proportion of fat in the diet can result in reductions of breast density, although the direct connection to risk has not yet been made. The relationship between breast density and hormone-related and genetic factors is also of great interest. It is often not possible or ethical to obtain mammograms on some groups of women for whom information on density would be very useful. This includes younger women as well as groups in which it would be desirable to obtain such information at frequent intervals. For this reason, we are exploring the use of imaging approaches such as ultrasound and magnetic resonance imaging, which do not require ionizing radiation, to make measurements analogous to those now being performed by using X-ray mammograms.

Adult↗

PubMed2006

Semi-automated segmentation and visualisation of outer bone cortex from medical images.

Good segmentation of the outer bone cortex from medical images is a prerequisite for applications in the field of finite element analysis, surgical planning environments and personalised, case dependent, bone reconstruction. However, current segmentation procedures are often unsatisfactory. This study presents an automated filter procedure to generate a set of adapted contours from which a surface mesh can be deduced directly. The degree of interaction is user determined. The bone contours are extracted from the patients CT data by quick grey value segmentation. An extended filter procedure then only retains contour information representing the outer cortex as more specific internal loops and shape irregularities are removed, tailoring the image for the above-mentioned applications. The developed medical image based design methodology to convert contour sets of multiple bone types, from tibia tumour to neurocranium, is reported and discussed.

Algorithms↗

PubMed2001

Automatic segmentation of cardiac magnetic resonance images using knowledge base.

OBJECTIVE: To study the automated implementation of cardiac magnetic resonance image (MRI) segmentation. METHODS: By training the feature parameters of the images and establishing a knowledge base, an efficient method for extracting and using prior knowledge was proposed. RESULTS and CONCLUSION: Through extracting and using prior knowledge of cardiac MRI, automation of cardiac MRI segmentation can be well accomplished.

Journal Article↗

PubMed2002

Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma.

OBJECTIVE: To develop an image analysis system for automated nuclear segmentation and classification of histologic bladder sections employing quantitative nuclear features. STUDY DESIGN: Ninety-two cases were classified into three classes by experienced pathologists according to the WHO grading system: 18 cases as grade 1, 45 as grade 2, and 29 as grade 3. Nuclear segmentation was performed by means of an artificial neural network (ANN)-based pixel classification algorithm, and each case was represented by 36 nuclei features. Automated grading of bladder tumor histologic sections was performed by an ANN classifier implemented in a two-stage hierarchic tree. RESULTS: On average, 95% of the nuclei were correctly detected. At the first stage of the hierarchic tree, classifier performance in discriminating between cases of grade 1 and 2 and cases of grade 3 was 89%. At the second stage, 79% of grade 1 cases were correctly distinguished from grade 2 cases. CONCLUSION: The proposed image analysis system provides the means to reduce subjectivity in grading bladder tumors and may contribute to more accurate diagnosis and prognosis since it relies on nuclear features, the value of which has been confirmed.

Algorithms↗

PubMed2006

Automated classification and quantification of F-actin-containing ruffles in confocal micrographs.

Membrane ruffles are actin-rich protrusions of the plasma membrane that can be observed on the surface of many cell types. Phase contrast and fluorescent microscopy are widely used in the analysis of ruffles, which are commonly identified in cells stained with fluorescently labeled phalloidin. Currently, comparison of cellular ruffle formation under different experimental conditions is generally qualitative or semiquantitative. Ruffle structures are often defined using manual tracing and thresholding methods. Here, we report the rapid and accurate segmentation of ruffles from two-dimensional confocal projections of cells using automated method based on well-established image processing and analysis methods. Line-shaped ruffles were detected using line detectors and were then separated from the filtered images. Automated categorizing of the segmented line structures enabled accurate quantification of the ruffles. This automated approach is efficient and reliable and hence can serve as a powerful tool in studies of the mechanism of ruffle formation.

Actins↗

PubMed2003

Automated segmentation and analysis of vascular structures in magnetic resonance angiographic images.

The accurate assessment of the presence and extent of vascular disease, and planning of vascular interventions based on MRA requires the determination of vessel dimensions. The current standard is based on measuring vessel diameters on maximum intensity projections (MIPs) using calipers. In order to increase the accuracy and reproducibility of the method, automated analysis of the 3D MR data is required. A novel method for automatically determining the trajectory of the vessel of interest, the luminal boundaries, and subsequent the vessel dimensions is presented. The automated segmentation in 3D uses deformable models, combined with knowledge of the acquisition protocol. The trajectory determination was tested on 20 in vivo studies of the abdomen and legs. In 93% the detected trajectory followed the vessel. The luminal boundary detection was validated on contrast-enhanced (CE) MRA images of five stenotic phantoms. The results from the automated analysis correlated very well with the true diameters of the phantoms used in the in vitro study (r = 0.999, P < 0.001). MRA and x-ray angiography (XA) of the phantoms also correlated well (r = 0.895, P < 0.001). The average unsigned difference between the MRA and XA measurements was 0.08 +/- 0.05 mm. In conclusion, the automated approach allows the accurate assessment of vessel dimensions in MRA images.

Abdomen↗

PubMed2025

MetaChrome: An Open-Source, User-Friendly Tool for Automated Metaphase Chromosome Analysis.

DNA Fluorescence In Situ Hybridization (FISH) is an essential technique to study chromosome biology and genetics, enabling precise visualization of specific genomic loci to study structural abnormalities, gene mapping, and chromosomal rearrangements. High-Throughput Imaging (HTI) can automate the analysis of DNA-FISH chromosome images, but the accurate and automated segmentation of mitotic chromosomes and simultaneous colocalization of FISH signals remains a challenge. While several commercial automated karyotyping tools partially solve these issues, open-source software that effectively combines robust chromosome segmentation with comprehensive colocalization analysis capabilities remains necessary. To address this unmet need, we developed MetaChrome, an open-source software platform built around a graphical user interface and explicitly designed for automated metaphase chromosome analysis. MetaChrome leverages fine-tuned deep learning models to automate metaphase chromosome segmentation, together with colocalization analysis of chromosome-specific FISH probes and immunofluorescent-labeled proteins. Importantly, MetaChrome achieves enhanced segmentation accuracy compared to traditional image processing methods by adopting a Cellpose segmentation model fine-tuned with manually annotated metaphase chromosome datasets. The fine-tuned model ensures precise assignment of DNA-FISH spots to individual chromosomes in an automated manner. This facilitates rapid identification of chromosomal abnormalities, reduces human error, and advances high-throughput chromosome analysis workflows, addressing a key bottleneck in chromosome biology research.

Chromosome segmentation↗