M. Kaus, S. Warfield, A. Nabavi, P. Black, F. Jolesz, and R. Kikinis, "Automated segmentation of MRI of brain tumors," Radiology 218(2), pp. 586--591, 2001. Home/Search Document Details and Download
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A Statistically Based Flow for Image Segmentation - Eric Pichon Allen (2004) Self-citation (Kikinis) (Correct)
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Kaus, M., Warfield, S., Nabavi, A., Black, P., Jolesz, F., Kikinis, R., 2001. Automated segmentation of MRI of brain tumors. Radiology 218 (2), 586--591.
Simultaneous Truth and Performance Level Estimation.. - Warfield, Zou, Wells (2004) Self-citation (Warfield) (Correct)
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M. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz, and R. Kikinis, "Automated Segmentation of MRI of Brain Tumors," Radiology, vol. 218, pp. 586--591, Feb 2001.
Statistical Validation of Automated Probabilistic.. - Zou, Wells, III.. (2002) Self-citation (Kaus Warfield Jolesz Kikinis) (Correct)
....5 A Clinical Example: MRI of Three Types of Brain Tumors 5. 1 Materials and Methods (1) The Cases: A total of nine patients were selected from a neurosurgical database of 260 brain tumor patients, of which three had meningiomas (M) three astrocytomas (A) and three other low grade gliomas (G)[10]. The meningiomas enhanced well but the gliomas did not. 2) Imaging Protocol: Patient heads were imaged in the sagittal planes with a 1.5T MR imaging system (Signa, GE Medical Systems, Milwaukee, WI) with a postcontrast 3D sagittal spoiled gradient recalled (SPGR) acquisition with contiguous ....
.... Probabilistic Segmentation: The automated probabilistic segmentation was the relative tumor probability of lesion per voxel with signal intensity modeled as a Gaussian mixture of the two classes based on an initial semi automated binary segmentation (left panel of Fig 1, in an example case) [10]. 4) Manual Segmentation and Composite Ground Truth: An interactive segmentation tool (MRX, GE Medical Systems, Schenectady, NY) was employed and ran on an Ultra 10 Workstation (Sun Microsystems, Mountain View, CA) The structures were outlined slice wise by expert operators using a mouse. The ....
Kaus, M., Warfield S. K., Nabavi A., Black, P. M., Jolesz, F. A., Kikinis, R.: Automated segmentation of MRI of brain tumors. Radiology 218 (2001) 586-591.
Validation of Image Segmentation and Expert Quality with.. - Warfield, Zou, Wells (2002) (16 citations) Self-citation (Warfield) (Correct)
....small parameter changes, and indicate how our algorithm resolves ambiguities between different experts in a natural way. We applied the method to the assessment of a previously published automated image segmentation algorithm [9] for the segmentation of brain tumors from magnetic resonance images [10]. We compared the performance of three experts to that of the segmentation algorithm on ten cases. Illustrative results are presented. We assessed multiple segmentations by a single expert for the task of identifying the prostate peripheral zone from magnetic resonance images. 2 Method We ....
....0.5 1.0 , 1.0, 1.0 8 1.0,0.0 2 0.9, 0.9 0.12 1.0 , 1.0, 1.0 4 1.0,0.0 3 0.9, 0.9, 0.9 0.12 0.88,0.99 , 1.0,1.0 , 0.88,0.99 11 1.0, 0. 0 Validation of Brain Tumor Segmentation from MRI Figure 1 illustrates our algorithm applied to the analysis of expert segmentations of a brain tumor [10]. The analysis indicates the program is generating segmentations similar to that of the experts, with higher sensitivity than one of the experts, but with lower sensitivity than two other experts. Validation of Prostate Peripheral Zone Segmentation from MRI Figure 2 illustrates our algorithm ....
M. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz, and R. Kikinis, "Automated Segmentation of MRI of Brain Tumors," Radiology, vol. 218, pp. 586--591, Feb 2001.
Simultaneous Validation of Image Segmentation and.. - Warfield, Zou, Kaus.. (2002) (2 citations) Self-citation (Kaus Warfield) (Correct)
....enables the assessment of an automated image segmentation algorithm, and direct comparison of expert and algorithm performance. We applied our validation method to the assessment of a previously published automated image segmentation algorithm [6] for the segmentation of brain tumors from MRI [7]. We compared the performance of three experts to that of the segmentation algorithm on ten cases. Illustrative results are presented. 2. METHOD We describe an algorithm for estimating the hidden ground truth and expert segmentation quality from a collection of segmentations by experts. Let T i ....
Michael Kaus, Simon K. Warfield, Arya Nabavi, Peter M. Black, Ferenc A. Jolesz, and Ron Kikinis, "Automated Segmentation of MRI of Brain Tumors," Radiology, vol. 218, pp. 586--591, Feb 2001.
A Laplace Equation Approach for Shape Comparison - Eric Pichon And (2006) (Correct)
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M. Kaus, S. Warfield, A. Nabavi, P. Black, F. Jolesz, and R. Kikinis, "Automated segmentation of MRI of brain tumors," Radiology 218(2), pp. 586--591, 2001.
Interactive, GPU-Based Level Sets for 3D Segmentation - Lefohn, al. (Correct)
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Kaus, M., Warfield, S.K., Nabavi, A., Black, P.M., Jolesz, F.A., Kikinis, R.: Automated segmentation of mri of brain tumors. Radiology 218 (2001) 586--591
Interactive, GPU-Based Level Sets for 3D Brain Tumor.. - Lefohn, Cates, Whitaker (2003) (Correct)
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M. Kaus, S. K. War eld, A. Nabavi, P. M. Black, F. A. Jolesz, and R. Kikinis, \Automated segmentation of mri of brain tumors," Radiology, vol. 218, pp. 586-591, 2001.
Interactive, GPU-Based Level Sets for 3D Brain Tumor.. - Lefohn, Cates, Whitaker (2003) (Correct)
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M. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz, and R. Kikinis, "Automated segmentation of mri of brain tumors," Radiology, vol. 218, pp. 586--591, 2001.
Case Study: An Evaluation of User-Assisted Hierarchical.. - Cates, Whitaker, Jones (2004) (Correct)
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M. Kaus, S. K. Warfield, A. Nabavi, P. M. Black, F. A. Jolesz, R. Kikinis, Automated segmentation of mri of brain tumors, Radiology 218 (2001) 586--591.
A New Deformable Model Using Dynamic Gradient Vector Flow - And Adaptive Balloon (2003) (Correct)
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M. Kaus, S. Warfield, A. Nabavi, P. Black, F. Jolesz, and R. Kikinis. Automated segmentation of mri of brain tumors. Radiology, 218:586--591, 2001.
GIST: An Interactive, GPU-Based Level Set Segmentation.. - Cates, Lefohn, Whitaker (2004) (Correct)
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Kaus, M., Warfield, S.K., Nabavi, A., Black, P.M., Jolesz, F.A., Kikinis, R.: Automated segmentation of mri of brain tumors. Radiology 218 (2001) 586--591 Joshua E. Cates, Aaron E. Lefohn , Ross T. Whitaker
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