Automatic Segmentation of Ventricular Cerebrospinal Fluid from Ischemic Stroke CT Images

Automatic Segmentation of Ventricular Cerebrospinal Fluid from Ischemic Stroke CT Images

Accurate segmentation of ventricular cerebrospinal fluid (CSF) regions in stroke CT images is important in assessing stroke patients. Manual segmentation is subjective, time consuming and error prone. There are currently no methods dedicated to extracting ventricular CSF regions in stroke CT images....

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Bibliographic Details
Published in:Neuroinformatics (Totowa, N.J.) Vol. 10; no. 2; pp. 159 - 172
Main Authors: Poh, L. E., Gupta, V., Johnson, A., Kazmierski, R., Nowinski, W. L.
Format: Journal Article
Language:English
Published: New York Springer-Verlag 01-04-2012
Springer Nature B.V
Subjects:
Algorithms
Bioinformatics
Biomedical and Life Sciences
Biomedicine
Brain Ischemia - cerebrospinal fluid
Brain Ischemia - complications
Brain Ischemia - diagnostic imaging
Cerebral Ventriculography - methods
Cerebrospinal fluid
Computational Biology/Bioinformatics
Computed tomography
Computer Appl. in Life Sciences
Fourth Ventricle - diagnostic imaging
Humans
Image processing
Image Processing, Computer-Assisted
Ischemia
Magnetic Resonance Imaging
Neurology
Neurosciences
Original Article
Reproducibility of Results
Scaling
Segmentation
Stroke
Stroke - cerebrospinal fluid
Stroke - diagnostic imaging
Stroke - etiology
Tomography, X-Ray Computed
Ventricles (cerebral)
CT
Stroke
Segmentation
Ventriclular system
Registration
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Description
Summary:Accurate segmentation of ventricular cerebrospinal fluid (CSF) regions in stroke CT images is important in assessing stroke patients. Manual segmentation is subjective, time consuming and error prone. There are currently no methods dedicated to extracting ventricular CSF regions in stroke CT images. 102 ischemic stroke CT scans (slice thickness between 3 and 6 mm, voxel size in the axial plane between 0.390 and 0.498 mm) were acquired. An automated template-based algorithm is proposed to extract ventricular CSF regions which accounts for the presence of ischemic infarct regions, image noise, and variations in orientation. First, template VT 2 is registered to the scan using landmark-based piecewise linear scaling and then template VT 1 is used to further refine the registration by partial segmentation of the fourth ventricle. A region of interest (ROI) is found using the registered VT 2 . Automated thresholding is then applied to the ROI and the artifacts are removed in the final phase. Sensitivity, dice similarity coefficient, volume error, conformity and sensibility of segmentation results were 0.74 ± 0.12, 0.8 ± 0.09, 0.16 ± 0.11, 0.45 ± 0.39, 0.88 ± 0.09, respectively. The processing time for a 512 × 512 × 30 CT scan takes less than 30 s on a 2.49 GHz dual core processor PC with 4 GB RAM. Experiments with clinical stroke CT scans showed that the proposed algorithm can generate acceptable results in the presence of noise, size variations and orientation differences of ventricular systems and in the presence of ischemic infarcts.
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ISSN:1539-2791
1559-0089
DOI:10.1007/s12021-011-9135-9