Automatic segmentation of cerebrospinal fluid, white and gray matter in unenhanced computed tomography images.
Rationale and Objectives: Although segmentation algorithms for cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM) on unenhanced computed tomographic (CT) images exist, there is no complete research in this area. To take into account poor image contrast and intensity variability on CT...
| Publicado en: | Academic Radiology Vol. 17; no. 11; pp. 1350 - 1359 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Nov2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104929252&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104929252 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Nov2010 vid: 17 iid: 11 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 104929252 NLM20634108 2010825569 10.1016/j.acra.2010.06.005 NLM20634108 104929252 ppf: 1350 ppct: 9 formats: tig: atl: Automatic segmentation of cerebrospinal fluid, white and gray matter in unenhanced computed tomography images. aug: au: Gupta V Ambrosius W Qian G Blazejewska A Kazmierski R Urbanik A Nowinski WL Gupta, Varsha Ambrosius, Wojciech Qian, Guoyu Blazejewska, Anna Kazmierski, Radoslaw Urbanik, Andrzej Nowinski, Wieslaw L affil: Biomedical Imaging Lab, Agency for Science, Technology and Research, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671 sug: subj: Brain Radiography Cerebrospinal Fluid Radiography Image Interpretation, Computer Assisted Methods Nerve Fibers Radiography Neurons Radiography Information Science Methods Tomography, X-Ray Computed Methods Adult Aged Aged, 80 and Over Algorithms Artificial Intelligence Contrast Media Female Human Image Enhancement Methods Male Middle Age Reproducibility of Results Sensitivity and Specificity Subtraction Technique Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female Male ab: Rationale and Objectives: Although segmentation algorithms for cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM) on unenhanced computed tomographic (CT) images exist, there is no complete research in this area. To take into account poor image contrast and intensity variability on CT scans, the aim of this study was to derive and validate a novel, automatic, adaptive, and robust algorithm. Materials and Methods: Unenhanced CT scans of normal subjects from two different centers were used. The algorithm developed uses adaptive thresholding, connectivity, and domain knowledge and is based on heuristics on the shape of CT histogram. The slope of the intensity histogram corresponding to the three-dimensional largest connected region in a variable CSF intensity range is tracked to determine the critical intensity, which serves as an initial classifier of CSF-WM. Thresholds of CSF, WM, and GM are then optimally derived to minimize classification overlap. Multiple, null, and erroneous classifications are resolved by applying domain knowledge. Results: The ground-truth regions with the minimal partial volume effect were used to evaluate segmentation results using the statistical markers. Average sensitivity, Dice index, and specificity, respectively, for the first center were 95.7%, 97.0%, and 98.6% for CSF; 96.1%, 97.3%, and 98.8% for WM; and 95.2%, 94.3%, and 92.8% for GM. The results were consistent for the second data center. Conclusions: The algorithm automatically identifies CSF, WM, and GM on unenhanced CT images with high accuracy, is robust to data from different scanners, does not require any parameter setting, and takes about 5 minutes in MATLAB to process a 512 × 512 × 30 scan. The algorithm has potential use in research and clinical applications. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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