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Results for “Automated image segmentation”

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PubMed2005

Macular segmentation with optical coherence tomography.

PURPOSE: To develop a software algorithm to perform automated segmentation of retinal layer structures on linear macular optical coherence tomography (StratusOCT; Carl Zeiss Meditec, Inc., Dublin, CA) scan images and to test its performance in discriminating normal from glaucomatous eyes in comparison with conventional circumpapillary nerve fiber layer (cpNFL) thickness measurement. METHODS: Four layer structures within the retina were defined: the macular nerve fiber layer (mNFL), the inner retinal complex (IRC; retinal ganglion cell [RGC] layer + inner plexiform and nuclear layers), outer plexiform layer (OPL), and outer retinal complex (ORC; outer nuclear layer + photoreceptor layer). Normal and glaucomatous eyes underwent fast macular map and fast NFL OCT scans. Linear macular images were analyzed using the developed algorithm, and the results were compared with the cpNFL thickness measurement. RESULTS: Forty-seven subjects (23 normal and 24 with glaucoma) were analyzed. mNFL, cpNFL, IRC, and the total retinal thicknesses were significantly greater in normal than in glaucomatous eyes (P < or = 0.0002; Wilcoxon), whereas OPL thickness did not show a significant difference (P = 0.46). ORC thickness was significantly greater in glaucomatous than normal eyes (P = 0.035). Areas under the receiver operator characteristic curve (AROCs) for discriminating normal from glaucomatous eyes were highest with mNFL + IRC (0.97) and lowest with OPL (0.56). AROCs for OPL and ORC were significantly smaller than those for mNFL, IRC, mNFL+IRC, and cpNFL (P < or = 0.01). AROCs for IRC, mNFL + IRC, and cpNFL were significantly larger than for retinal thickness (P < or = 0.049). Among the best-performing parameters (mNFL, IRC, mNFL + IRC, and cpNFL) there was no significant difference in AROCs (P > or = 0.15). CONCLUSIONS: The newly developed macular segmentation algorithm described herein demonstrated its ability to quantify objectively the glaucomatous damage to RGCs and NFL and to discriminate between glaucomatous and normal eyes. Further algorithm refinement and improvements in resolution and image quality may yield a more powerful methodology for clinical glaucoma evaluation.

Algorithms↗

PubMed2026

PAT: An Image Analysis Tool for Automated Scoring of Pollen in Alexander-Stained Anthers.

Quantitative pollen viability analysis is a critical but labor-intensive step in plant reproductive biology. Existing deep-learning Segment Anything Models (SAM) fail to reliably segment viable pollen in Alexander-stained anthers. To address this, we fine-tuned an existing Cellpose-SAM model for pollen segmentation. We integrated it into PAT (Pollen Analysis Tool), a cross-platform desktop application. PAT features instance segmentation with interactive quality control, an in-app model retraining module, and publication-ready statistical outputs. We deployed PAT in an EMS suppressor screen of semi-sterile Arabidopsis smg7-6 mutants, enabling efficient candidate prioritization for whole-genome sequencing and mapping of the candidate mutation. This screen led to the identification of a point mutation in CAP-D2 (capd2-2), a Condensin I subunit, that rescues the smg7-6 meiotic phenotype. Notably, mutation in a Condensin II subunits (CAP-D3 and CAP-H2) does not confer rescue. Further characterization suggests the capd2-2 allele is hypomorphic, showing no defects in vegetative growth, chromocenter compaction, or transposable element silencing. Collectively, we demonstrate that accessible AI tools have the potential to bridge gaps in plant phenotyping and accelerate the pace of biological discovery.

Alexander staining↗

PubMed2006

In vivo magnetic resonance imaging and semiautomated image analysis extend the brain phenotype for cdf/cdf mice.

Magnetic resonance imaging and computer image analysis in human clinical studies effectively identify abnormal neuroanatomy in disease populations. As more mouse models of neurological disorders are discovered, such an approach may prove useful for translational studies. Here, we demonstrate the effectiveness of a similar strategy for mouse neuroscience studies by phenotyping mice with the cerebellar deficient folia (cdf) mutation. Using in vivo multiple-mouse magnetic resonance imaging for increased throughput, we imaged groups of cdf mutant, heterozygous, and wild-type mice and made an atlas-based segmentation of the structures in 15 individual brains. We then performed computer automated volume measurements on the structures. We found a reduced cerebellar volume in the cdf mutants, which was expected, but we also found a new phenotype in the inferior colliculus and the olfactory bulbs. Subsequent local histology revealed additional cytoarchitectural abnormalities in the olfactory bulbs. This demonstrates the utility of anatomical magnetic resonance imaging and semiautomated image analysis for detecting abnormal neuroarchitecture in mutant mice.

Animals↗

PubMed2004

Quantitative evaluation of a pulmonary contour segmentation algorithm in X-ray computed tomography images.

RATIONALE AND OBJECTIVES: Pulmonary contour extraction from thoracic x-ray computed tomography images is a mandatory preprocessing step in many automated or semiautomated analysis tasks. This study was conducted to quantitatively assess the performance of a method for pulmonary contour extraction and region identification. MATERIALS AND METHODS: The automatically extracted contours were statistically compared with manually drawn pulmonary contours detected by six radiologists on a set of 30 images. Exploratory data analysis, nonparametric statistical tests, and multivariate analysis were used, on the data obtained using several figures of merit, to perform a study of the interobserver variability among the six radiologists and the contour extraction method. The intraobserver variability of two human observers was also studied. RESULTS: In addition to a strong consistency among all of the quality indexes used, a wider interobserver variability was found among the radiologists than the variability of the contour extraction method when compared with each radiologist. The extraction method exhibits a similar behavior (as a pulmonary contour detector), to the six radiologists, for the used image set. CONCLUSION: As an overall result of the application of this evaluation methodology, the consistency and accuracy of the contour extraction method was confirmed to be adequate for most of the quantitative requirements of radiologists. This evaluation methodology could be applied to other scenarios.

Algorithms↗

PubMed2003

Pulmonary nodule detection using chest CT images.

PURPOSE: Automated methods for the detection of pulmonary nodules and nodule volume calculation on CT are described. MATERIAL AND METHODS: Gray-level threshold methods were used to segment the thorax from the background and then the lung parenchyma from the thoracic wall and mediastinum. A deformable model was applied to segment the lung boundaries, and the segmentation results were compared with the thresholding method. The lesions that had high gray values were extracted from the segmented lung parenchyma. The selected lesions included nodules, blood vessels and partial volume effects. The discriminating features such as size, solid shape, average, standard deviation and correlation coefficient of selected lesions were used to distinguish true nodules from pseudolesions. With texture features of true nodules, the contour-following method, which tracks the segmented lung boundaries, was applied to detect juxtapleural nodules that were contiguous to the pleural surface. Volume and circularity calculations were performed for each identified nodule. The identified nodules were sorted in descending order of volume. These methods were applied to 827 image slices of 24 cases. RESULTS: Computer-aided diagnosis gave a nodule detection sensitivity of 96% and no false-positive findings. CONCLUSION: The computer-aided diagnosis scheme was useful for pulmonary nodule detection and gave characteristics of detected nodules.

Diagnosis, Computer-Assisted↗

PubMedDate not supplied

A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.

Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a computer screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.

Algorithms↗

PubMed2006

Quantitative analysis of the epithelial lining architecture in radicular cysts and odontogenic keratocysts.

BACKGROUND: This paper describes a quantitative analysis of the cyst lining architecture in radicular cysts (of inflammatory aetiology) and odontogenic keratocysts (thought to be developmental or neoplastic) including its 2 counterparts: solitary and associated with the Basal Cell Naevus Syndrome (BCNS). METHODS: Epithelial linings from 150 images (from 9 radicular cysts, 13 solitary keratocysts and 8 BCNS keratocysts) were segmented into theoretical cells using a semi-automated partition based on the intensity of the haematoxylin stain which defined exclusive areas relative to each detected nucleus. Various morphometrical parameters were extracted from these "cells" and epithelial layer membership was computed using a systematic clustering routine. RESULTS: Statistically significant differences were observed across the 3 cyst types both at the morphological and architectural levels of the lining. Case-wise discrimination between radicular cysts and keratocyst was highly accurate (with an error of just 3.3%). However, the odontogenic keratocyst subtypes could not be reliably separated into the original classes, achieving discrimination rates slightly above random allocations (60%). CONCLUSION: The methodology presented is able to provide new measures of epithelial architecture and may help to characterise and compare tissue spatial organisation as well as provide useful procedures for automating certain aspects of histopathological diagnosis.

Journal Article↗

PubMed1989

Fully automated measurements by light microscopy of tissue sections using a cellular array computer.

Software was developed for the acquisition, segmentation and analysis of microscopic OD-images on a VICOM digital image processor, extended with a VISIOMORPH morphoprocessor board. The delineation algorithms for peroxisomes, lysosomes, and nuclei in liver, kidney, and adrenal gland sections start by thresholding the difference between the original image and a low pass filtered version. The resulting binary mask is then processed by morphological operations in order to produce an object overlay. The efficiency of the programs is evaluated by comparing delineated objects at different OD-levels, created by varying the stain or by multiplying the original pixel values with constant factors. Manual delineation on some images is also used as a reference. More complex algorithms are used for the delineation of muscle fibres in ATP-ase-stained sections and immunocytochemically labelled cells in monolayer preparations. Muscle images from parallel sections with different stainings are matched with a coordinate transform, enabling the transfer of the object mask from a single delineated image to the unprocessed images and thus obtain all necessary information for fibre classification. After segmentation, the OD-images and their object overlays are fed into a data extraction program, measuring for each delineated object user-selected features. Data are sent to a VAX for statistical interpretation.

Adrenal Cortex↗

PubMed2006

Optimum template selection for atlas-based segmentation.

Atlas-based segmentation of MR brain images typically uses a single atlas (e.g., MNI Colin27) for region identification. Normal individual variations in human brain structures present a significant challenge for atlas selection. Previous researches mainly focused on how to create a specific template for different requirements (e.g., for a certain population). We address atlas selection with a different approach: instead of choosing a fixed brain atlas, we use a family of brain templates for atlas-based segmentation. For each subject and each region, the template selection method automatically chooses the 'best' template with the highest local registration accuracy, based on normalized mutual information. The region classification performances of the template selection method and the single template method were quantified by the overlap ratios (ORs) and intraclass correlation coefficients (ICCs) between the manual tracings and the respective automated labeled results. Two groups of brain images and multiple regions of interest (ROIs), including the right anterior cingulate cortex (ACC) and several subcortical structures, were tested for both methods. We found that the template selection method produced significantly higher ORs than did the single template method across all of the 13 analyzed ROIs (two-tailed paired t-test, right ACC at t(8)=4.353, p=0.0024; right amygdala, matched paired t test t(8)>3.175, p<0.013; for the remaining ROIs, t(8)=4.36, p<0.002). The template selection method also provided more reliable volume estimates than the single template method with increased ICCs. Moreover, the improved accuracy of atlas-based segmentation using optimum templates approaches the accuracy of manual tracing, and thus is valid for automated brain imaging analyses.

Adult↗

PubMed1994

Automated extraction, labelling and analysis of the coronary vasculature from arteriograms.

For clinical decision-making and documentation purposes we have developed techniques to extract, label and analyze the coronary vasculature from arteriograms in an automated, quantitative manner. Advanced image processing techniques were applied to extract and analyze the vasculatures from non-subtracted arteriograms while artificial intelligence techniques were employed to assign anatomical labels. Lumen diameters of 11 phantom vessels were assessed with an accuracy of 0.27 +/- 0.19 mm (dtrue = 0.45 + 0.92dmeasured; r > 0.99) and 0.21 +/- 0.15 mm (dtrue = 0.42 + 0.91dmeasured; r > 0.99), from cine and digital images, respectively. We collected a total of 15 routinely acquired cine-arteriograms showing 74 vessel segments with 18 stenoses (severity larger than 30% assessed quantitatively), and 53 digital arteriograms showing 236 vessel segments with 69 stenoses. From the cine arteriograms we extracted 64 (86%) of the vessel segments without manual correction and 196 (83%) from the digital arteriograms. Repeated analysis (3 times) of the arteriograms by the same operator resulted in a standard deviation of the mean segment diameters (precision) of 0.064 mm for the cine-images and 0.020 mm for the digital images, while the standard deviations in the measurement of the minimal luminal diameter of the observed stenoses were 0.020 mm and 0.019 mm, respectively. The LAD artery, the septal and diagonal branches were correctly identified automatically in 86% of the segments. From these evaluations we conclude that our automated approach provides reliable tools for the assessment of multi-vessel disease, both in an off- and on-line environment.

Cineangiography↗

PubMed2005

Computerized segmentation of whole-body bone scintigrams and its use in automated diagnostics.

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor image resolution and artefacts necessitate that algorithms use sufficient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. A robust knowledge based methodology for detecting reference points of the main skeletal regions that is simultaneously applied on anterior and posterior whole-body bone scintigrams is presented. Expert knowledge is represented as a set of parameterized rules which are used to support standard image-processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is, to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is applied to automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians.

Algorithms↗

PubMed1998

Computer-assisted myocardial thickening analysis of gated MIBI SPECT images.

RATIONALE AND OBJECTIVES: The purpose of this study is to use the relationship between the partial volume effect, count density, matrix size, and reconstruction filter in gated single photon emission computer tomography (SPECT) to recover myocardial thickness, and to validate measurements generated using a computer-assisted automatic contour method with a phantom model and with thickness changes measured by echocardiography. METHODS: Regional myocardial contour was defined automatically using shape constraints, gray level thresholding, and a gradient method applied to gated technetium-99m (99mTc)-methoxy-isobutyl-isonitile (MIBI) SPECT images. A heart phantom with wall thickness ranging from 0.8 cm to 1.3 cm was constructed. SPECT images were acquired and reconstructed using different matrix sizes, count densities, and filters. Wall thickness determinations derived from the reconstructions made with each combination of matrix size, type of filter, and cutoff frequency were correlated with the measured thicknesses. The best combination was applied to the gated MIBI SPECT images of 55 patients who also had echocardiography and coronary arteriography. Count density measurements were made across each regional myocardial segment to evaluate wall thickness at end-diastole and end-systole. Systolic wall thickening measurements made with this automated computer-assisted contour technique were compared with echocardiographic measurements made from segments with normal perfusion and from regions containing infarcted tissue. RESULTS: Different reconstruction filters and cutoff frequencies affected the accuracy of measurements of myocardial wall thickness determined from gated myocardial SPECT images. A matrix size of 64 x 64, with a minimum of 10 counts/pixel/frame, and use of a Hanning filter with 0.5 cyc/cm cutoff frequency gave the best combination for myocardial thickness determination and spatial resolution. Application of these factors to the phantom yielded results that correlated very well with the thickness measurements (r = 0.986, P < 0.001). Application of the technique to the clinical SPECT studies yielded measurements of myocardial wall thickening that were not significantly different from that determined by echocardiography. CONCLUSION: Computer-assisted contour analysis of gated SPECT images enables accurate determination of regional wall thickening using the count density changes within each myocardial segment.

Analysis of Variance↗

PubMed1999

Compression of the prechiasmatic optic nerve produces a junctional scotoma.

PURPOSE: To demonstrate the clinical-radiologic correlation between a junctional scotoma and a focal lesion compressing the prechiasmatic segment of the distal optic nerve. METHODS: Case report involving a man with a pituitary adenoma. Clinical correlation was determined by reviewing visual field evaluations and magnetic resonance images. RESULTS: The tumor compressed the prechiasmatic segment of the distal optic nerve but not the optic chiasm, producing a junctional scotoma documented by Goldmann and automated perimetry. The visual field defect resolved after neurosurgical decompression of the anterior visual pathway. CONCLUSIONS: A junctional scotoma can be caused by focal as well as large and diffuse lesions injuring the anterior visual pathway, specifically at the junction of the optic nerve and chiasm. This finding supports the existence of Wilbrand fibers.

Adenoma↗

PubMed2006

A completely automated CAD system for mass detection in a large mammographic database.

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Algorithms↗

PubMed1995

Prostatic intraepithelial neoplasia. Quantitation of the basal cell layer with machine vision system.

The aim of this paper is to report on a fully automated procedure to quantify the epithelial cell components in prostatic intraepithelial neoplasia with particular emphasis on the basal cell layer (BCL). Scene segmentation was guided by a knowledge-based system of digitized images recorded from histological section immunostained against high molecular weight keratin and counterstained with hematoxylin and eosin. Scene segmentation involved two major stages. First, the system located and identified the duct and segmented the scene, resulting in "intermediate segmentation products." This stage was followed by a reconstruction process in which the segmentation products (i.e., the lumen and the darkly and lightly stained epithelial cell components) were assembled to form the microscopic structure to achieve working unity. Following this, histometric measurements were made of the reconstructed scene. Computed were the percentage of the duct contour with BCL (90%), and the number and length of gaps in the BCL (19, ranging from 10 to 90 microns). Automated analysis of the BCL is accurate and provides information not readily identified by human examination.

Basement Membrane↗

PubMed1999

Reconstruction of the human cerebral cortex from magnetic resonance images.

Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. In this paper we address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.

Algorithms↗

PubMed2006

Computerized craniofacial reconstruction using CT-derived implicit surface representations.

In forensic craniofacial reconstruction, facial features of an unknown individual are estimated from an unidentified skull, based on a mixture of experimentally obtained guidelines on the relationship between soft tissues and the underlying skeleton. In this paper, we investigate the possibility of using full 3D cross-sectional CT images for establishing a reference database of densely sampled distances between the external surfaces of the skull and head for automated craniofacial reconstruction. For each CT image in the reference database, the hard tissue (skull) and soft tissue (head) volumes are automatically segmented and transformed into signed distance transform (sDT) images, representing for each voxel in this image the Euclidean distance to the closest point on the skull and head surface, respectively, distances being positive (negative) for voxels inside (outside) the skull/head. Multiple craniofacial reconstructions are obtained by first warping each reference skull sDT maps to the target skull sDT using a B-spline based free form deformation algorithm and subsequently applying these warps to the reference head sDT maps. A single reconstruction of the target head surface is defined as the zero level set of the arithmetic average of all warped reference head sDT maps, but other reconstructions are possible, biasing the result to subject specific attributes (age, BMI, gender). Both qualitative and quantitative tests (measuring the similarity between the 3D reconstructed and corresponding original head surface) on a small (N = 20) database are presented to proof the validity of the concept.

Databases as Topic↗

PubMedDate not supplied

A method for fully automated quantitative analysis of arterial flow using flow-sensitized MR images.

While the recent developments in the velocity-sensitive MR imaging offer great potential for non-invasive assessment of blood flow in major blood vessels, the clinical applications of this technique have been hampered by tedious, human-assisted data processing techniques. In this paper, we describe a robust system for automated extraction of quantitative as well as qualitative flow information from velocity-sensitive, phase contrast MR images. The algorithm accomplishes reliable segmentation of blood vessels using multiresolution analysis based on wavelet transform, and employs a multivariate scoring criterion to minimize the impact of imaging artifacts such as partial volume averaging and flow turbulence, which normally cause incomplete or inaccurate detection of vascular boundaries.

Algorithms↗