Measurement of Apparent Diffusion Coefficient Values from Diffusion-Weighted MRI: A Comparison of Manual and Semiautomatic Segmentation Methods
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Purpose: To compare the interobserver and intraobserver reliability of mean apparentdiffusion coefficient (ADC) values using contrast-enhanced (CE) T1 weightedimage (WI) and T2WI as structural images between manual and semiautomaticsegmentation methods. Materials and Methods: Between January 2011 and May 2013, 28 patients whounderwent brain MR with diffusion weighted image (DWI) and were pathologicallyconfirmed as having glioblastoma participated in our study. The ADC values weremeasured twice in manual and semiautomatic segmentation methods using CE-T1WIand T2WI as structural images to obtain interobserver and intraobserver reliability. Moreover, intraobserver reliabilities of the different segmentation methods wereassessed after subgrouping of the patients based on the MR findings. Results: Interobserver and intraobserver reliabilities were high in both manualand semiautomatic segmentation methods on CE-T1WI-based evaluation, whileinterobserver reliability on T2WI-based evaluation was not high enough to be used ina clinical context. The intraobserver reliability was particularly lower with the T2WIbasedsemiautomatic segmentation method in the subgroups with involved lobes ≤2, with partially demarcated tumor borders, poorly demarcated inner margins of thenecrotic portion, and with perilesional edema. Conclusion: Both the manual and semiautomatic segmentation methods on CET1WI-based evaluation were clinically acceptable in the measurement of mean ADCvalues with high interobserver and intraobserver reliabilities.
Abstract
Purpose: To compare the interobserver and intraobserver reliability of mean apparentdiffusion coefficient (ADC) values using contrast-enhanced (CE) T1 weightedimage (WI) and T2WI as structural images between manual and semiautomaticsegmentation methods. Materials and Methods: Between January 2011 and May 2013, 28 patients whounderwent brain MR with diffusion weighted image (DWI) and were pathologicallyconfirmed as having glioblastoma participated in our study. The ADC values weremeasured twice in manual and semiautomatic segmentation methods using CE-T1WIand T2WI as structural images to obtain interobserver and intraobserver reliability. Moreover, intraobserver reliabilities of the different segmentation methods wereassessed after subgrouping of the patients based on the MR findings. Results: Interobserver and intraobserver reliabilities were high in both manualand semiautomatic segmentation methods on CE-T1WI-based evaluation, whileinterobserver reliability on T2WI-based evaluation was not high enough to be used ina clinical context. The intraobserver reliability was particularly lower with the T2WIbasedsemiautomatic segmentation method in the subgroups with involved lobes ≤2, with partially demarcated tumor borders, poorly demarcated inner margins of thenecrotic portion, and with perilesional edema. Conclusion: Both the manual and semiautomatic segmentation methods on CET1WI-based evaluation were clinically acceptable in the measurement of mean ADCvalues with high interobserver and intraobserver reliabilities.
참고문헌 (22)
- Schwartzbaum JA, "Epidemiology and molecular pathology of glioma", Nat Clin Pract Neurol, 2, pp.494~503 (2006)
- Law M, "Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas", AJNR Am J Neuroradiol, 28, pp.761~766 (2007)
- Sugahara T, "Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas", J Magn Reson Imaging, 9, pp.53~60 (1999)
- Kang Y, "Gliomas: Histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging--correlation with tumor grade", Radiology, 261, pp.882~890 (2011)
- Provenzale JM, "Diffusionweighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response", Radiology, 239, pp.632~649 (2006)
- Murakami R, "Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method", Radiology, 251, pp.838~845 (2009)
- Murakami R, "Magnetic resonance imaging of pilocytic astrocytomas: usefulness of the minimum apparent diffusion coefficient (ADC) value for differentiation from high-grade gliomas", Acta Radiol, 49, pp.462~467 (2008)
- Huo J, "CADrx for GBM brain tumors: predicting treatment response from changes in diffusion-weighted MRI", Algorithms, 2, pp.1350~1367 (2009)
- Pope WB, "Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment", Radiology, 252, pp.182~189 (2009)
- Fletcher-Heath LM, "Automatic segmentation of non-enhancing brain tumors in magnetic resonance images", Artif Intell Med, 21, pp.43~63
- Clark MC, "Automatic tumor segmentation using knowledge-based techniques", IEEE Trans Med Imaging, 17, pp.187~201 (1998)
- Nie J, "Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field", Comput Med Imaging Graph, 33, pp.431~441 (2009)
- Kaus MR, "Automated segmentation of MR images of brain tumors", Radiology, 218, pp.586~591 (2001)
- Corso JJ, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification", IEEE Trans Med Imaging, 27, pp.629~640 (2008)
- Bjornerud A, "The ICE software package: direct coregistration of anatomical and functional datasets using DICOM image geometry information", Proc Hum Brain Mapping, 19, pp.1018p~ (2003)
- Sundar H, "Robust computation of mutual information using spatially adaptive meshes", Med Image Comput Comput Assist Interv, 10, pp.950~958 (2007)
- Pluim JP, "Mutual-informationbased registration of medical images: a survey", IEEE Trans Med Imaging, 22, pp.986~1004 (2003)
- Reed GF, "Use of coefficient of variation in assessing variability of quantitative assays", Clin Diagn Lab Immunol, 9, pp.1235~1239 (2002)
- Jung SC, "Cerebral blood volume analysis in glioblastomas using dynamic susceptibility contrast-enhanced perfusion MRI: a comparison of manual and semiautomatic segmentation methods", PLoS One, 8, pp.e69323~ (2013)
- Emblem KE, "Automatic glioma characterization from dynamic susceptibility contrast imaging: brain tumor segmentation using knowledge-based fuzzy clustering", J Magn Reson Imaging, 30, pp.1~10 (2009)
- Prastawa M, "Automatic brain tumor segmentation by subject specific modification of atlas priors", Acad Radiol, 10, pp.1341~1348 (2003)
- Hsieh TM, "Automatic segmentation of meningioma from noncontrasted brain MRI integrating fuzzy clustering and region growing", BMC Med Inform Decis Mak, 11, pp.54~ (2011)