BACKGROUND AND PURPOSE: Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference. METHODS: Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan. RESULTS: Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not. CONCLUSION: The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well.
BACKGROUND AND PURPOSE: Two 3D image analysis algorithms, nearest-neighbor tissue segmentation and surface modeling, were applied separately to serial MR images in patients with glioblastoma multiforme (GBM). Rates of volumetric change were tracked for contrast-enhancing tumor tissue. Our purpose was to compare the two image analysis algorithms in their ability to track tumor volume relative to a manually defined standard of reference. METHODS: Three-dimensional T2-weighted and contrast-enhanced T1-weighted spoiled gradient-echo MR volumes were acquired in 10 patients with GBM. One of two protocols was observed: 1) a nearest-neighbor algorithm, which used manually determined or propagated tags and automatically segmented tissues into specific classes to determine tissue volume; or 2) a surface modeling algorithm, which used operator-defined contrast-enhancing boundaries to convert traced points into a parametric mesh model. Volumes were automatically calculated from the mesh models. Volumes determined by each algorithm were compared with the standard of reference, generated by manual segmentation of contrast-enhancing tissue in each cross section of a scan. RESULTS: Nearest-neighbor algorithm enhancement volumes were highly correlated with manually segmented volumes, as were growth rates, which were measured in terms of halving and doubling times. Enhancement volumes generated by the surface modeling algorithm were also highly correlated with the standard of reference, although growth rates were not. CONCLUSION: The nearest-neighbor tissue segmentation algorithm provides significant power in quantifying tumor volume and in tracking growth rates of contrast-enhancing tissue in patients with GBM. The surface modeling algorithm is able to quantify tumor volume reliably as well.
Authors: L P Clarke; R P Velthuizen; M Clark; J Gaviria; L Hall; D Goldgof; R Murtagh; S Phuphanich; S Brem Journal: Magn Reson Imaging Date: 1998-04 Impact factor: 2.546
Authors: T Nariai; M Senda; K Ishii; T Maehara; S Wakabayashi; H Toyama; K Ishiwata; K Hirakawa Journal: J Nucl Med Date: 1997-10 Impact factor: 10.057
Authors: L P Clarke; R P Velthuizen; S Phuphanich; J D Schellenberg; J A Arrington; M Silbiger Journal: Magn Reson Imaging Date: 1993 Impact factor: 2.546
Authors: M Vaidyanathan; L P Clarke; R P Velthuizen; S Phuphanich; A M Bensaid; L O Hall; J C Bezdek; H Greenberg; A Trotti; M Silbiger Journal: Magn Reson Imaging Date: 1995 Impact factor: 2.546
Authors: Olivier Clatz; Maxime Sermesant; Pierre-Yves Bondiau; Hervé Delingette; Simon K Warfield; Grégoire Malandain; Nicholas Ayache Journal: IEEE Trans Med Imaging Date: 2005-10 Impact factor: 10.048
Authors: Anne Line Stensjøen; Ole Solheim; Kjell Arne Kvistad; Asta K Håberg; Øyvind Salvesen; Erik Magnus Berntsen Journal: Neuro Oncol Date: 2015-03-10 Impact factor: 12.300