PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10724914

Computed tomography image analyzer: segmentation applying active contour models--"snakes".

R Maksimovic·S Stankovic·D Milovanovic·J Marinkovic·B Goldner·M Janicijevic·P M Seferovic
View original source ↗Export metadata ↓

Abstract

Many diagnostic and therapeutic procedures depend on medical images. In order to overcome imperfections of obtained images which are due to acquisition process and to obtain new information from available images, many techniques have been developed. In this study relatively new method of image segmentation, active contour model--"snakes" was applied in analyzing computed tomography (CT) images in patients with acute head trauma. Using this method, lesion to brain (LBR) and ventricle to brain ratio (VBR) were obtained accurately. Quantitative variable LBR, is significantly higher in patients with other pathologic CT findings and who do not survive during hospitalization. Thus, by applying segmentation "snakes" model it is possible to extract maximum information from available CT scans. These variables could be basis for medical decision making in patients with acute head trauma.

Explore related subjects

ArtifactsBrainBrain InjuriesCerebral VentriclesCerebral VentriculographyDecision Support TechniquesHumansImage Processing, Computer-AssistedTomography, X-Ray ComputedJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R Maksimovic, S Stankovic, D Milovanovic, J Marinkovic, B Goldner, M Janicijevic, P M Seferovic. 1999. Computed tomography image analyzer: segmentation applying active contour models--"snakes".. https://pubmed.ncbi.nlm.nih.gov/10724914/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Striping artifact removal in VisiumHD data through nuclear counts modeling.

MOTIVATION: 10x Genomics VisiumHD enables spatial transcriptomics at 2 µm × 2 µm resolution but exhibits slide-specific, non-periodic striping artifacts due to lane-width variability. These multiplicative row/column effects distort bin total counts and can bias downstream analyses. The state-of-the-art destriping approach is the normalization procedure used as a preprocessing step in bin2cell; it applies sequential high-quantile row- then column-wise normalization, which is asymmetric and can introduce edge effects/macro-stripes and distortions of large-scale total-count structure. RESULTS: We propose a statistical destriping approach that leverages nuclei segmentation from the co-registered H&E image. Assuming transcript abundance is constant within each nucleus, we model bin counts with a negative binomial distribution whose mean is a product of a nucleus-specific concentration and row- and column-specific stripe-factors reflecting lane-width variation. We fit all parameters in a generalized linear modeling framework with cross-validated regularization on stripe-factors and iterative dispersion estimation, and use the fitted parameters to correct the observed counts into a destriped image. On synthetic data with known ground truth, our method improves stripe-factor estimation accuracy and reduces error in corrected counts relative to bin2cell and bin2cell-derived baselines. Across four public VisiumHD slides, it consistently lowers striping intensity while substantially better preserving biological signal present in the large-scale global count structure and avoiding the artifacts introduced by other methods. AVAILABILITY AND IMPLEMENTATION: All source code and links to publicly available data used for this study are available at https://github.com/paolamalsot/destriping-GLM.

Artifacts↗
PubMed2003

LC-MS and NMR determination of a dichloromethane artifact adduct, cyproheptadine chloromethochloride.

The British Pharmacopoeia (BP) monograph for cyproheptadine HCl tablets requires a 'Related substances' thin-layer chromatography (TLC) test. This test revealed an extraneous spot with an R(f) of 0.1 in certain cyproheptadine HCl tablets that were under ambient retention conditions as well as those on stability programs. An investigation utilizing LC-MS, direct infusion MS, NMR, and organic synthesis has identified that the spot results from the N-oxide of cyproheptadine (a genuine degradate) and a co-eluting cyproheptadine-dichloromethane adduct, an artifact formed during the sample extraction step in which dichloromethane is used in the extracting solvent.

Artifacts↗
PubMed2003

Detection of beta-lactamase reporter gene expression by flow cytometry.

BACKGROUND: Flow cytometry of gene expression in living cells requires accurate, sensitive, nontoxic fluorescent indicators capable of detecting transcription of specific genes. This is typically achieved by using genes that encode fluorescent proteins or enzymes coupled to promoters of interest. The most commonly used reporters are green fluorescent protein and beta-galactosidase (lacZ). In this study, we characterized the performance of a cell-permeant, ratiometric, beta-lactamase substrate, coumarin cephalosporin fluorescein (CCF2/AM). We compared its characteristics with that of the beta-galactosidase/fluorescein di-beta-D-galactopyranoside reporter system. METHODS: Jurkat cell lines were generated for beta-lactamase and beta-galactosidase reporters with the use of similar plasmid constructs. Rare event flow cytometric detection for the beta-galactosidase and beta-lactamase reporters were assayed by using mixed populations of negative (WT) and positive (constitutively expressing) cells for each reporter. To determine sensitivity at low reporter copy number, we measured the activity of an unstimulated inducible promoter and detected positive events as a function of substrate incubation time. Technical issues related to data processing and optical configuration are also presented. RESULTS: The low population coefficients of variation afforded by ratiometric detection of the beta-lactamase system improved the statistical performance of the assay in comparison with a single-dye, intensity-based assay, leading to markedly improved detection for low copy number and rare events. At low levels of gene expression, beta-lactamase was detected with approximately 10-fold higher confidence than was beta-galactosidase. In rare event detection experiments, cells expressing high levels of beta-lactamase proteins were reliably detected at frequencies of 1:10(6) compared with about 1:10(4) for beta-galactosidase. CONCLUSION: The ratiometric fluorescence readout of the beta-lactamase system based on fluorescence resonance energy transfer allowed more sensitive and accurate detection of gene expression than the currently available beta-galactosidase substrates. Further, the cell-permeant nature of the substrate improved experimental convenience. These properties facilitated cell engineering and enabled a variety of applications including selection of rare cells from large populations and measurement of low-expressing or downregulated genes.

Artifacts↗