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A new computer-aided method for detecting brain metastases on contrast-enhanced MR images
1. | Department of Computational Science and Engineering, Yonsei University, South Korea, South Korea, South Korea |
2. | Department of Brain and Cognitive Engineering, Korea University, South Korea |
References:
[1] |
R. D. Ambrosini, P. Wang and W. G. O'Dell, Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching,, J. Magn. Reson. Imaging., 31 (2010), 85.
doi: 10.1002/jmri.22009. |
[2] |
T. Chan and L. Vese, Active Contours Without Edges,, IEEE Trans. Image Proc., 10 (2001), 266.
doi: 10.1109/83.902291. |
[3] |
K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,, Comput Med Imaging Graph Epub, 31 (2007), 198.
doi: 10.1016/j.compmedimag.2007.02.002. |
[4] |
K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging,, Elsevier Science Pub, (1999). Google Scholar |
[5] |
R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor,, ICGST-GVIP Journal, 9 (2009), 33. Google Scholar |
[6] |
D. Finelli, G. Hurst, R. Gullapali and E. Bellon, Improved contrast of enhancing brain lesions on postgadolinium, T1-weighted spin-echo images with use of magnetization transfer,, Radiology, 190 (1994), 553. Google Scholar |
[7] |
C. I. Henschke, D. F. Yankelevitz, I. Mateescu, D. W. Brettle, T. G. Rainey and F. S. Weingard, Neural networks for the analysis of small pulmonary nodules,, Clin Imaging., 21 (1997), 390.
doi: 10.1016/S0899-7071(97)81731-7. |
[8] |
J. Jagannathan, J. H. Sherman, G. U. Mehta GU and L. S. Chin, Radiobiology of brain metastasis: Applications in stereotactic radiosurgery,, Neurosurg Focus, 22 (2007), 1.
doi: 10.3171/foc.2007.22.3.5. |
[9] |
Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,, IEEE Trans. Med. Imaging, 20 (2001), 595. Google Scholar |
[10] |
A. Madabhushi, M. D. Feldman, D. N. Metaxas, J. Tomaszeweski and D. Chute, Automated detection of prostatic adenocarcinoma from high-resolution ex vivo mri,, IEEE Trans. Med. Imaging, 24 (2005), 1611.
doi: 10.1109/TMI.2005.859208. |
[11] |
M. F. McNitt-Gray, E. M. Hart, N. Wyckoff, J. W. Sayre, J. G. Goldin and D. R. Aberle, A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution ct: Preliminary results,, Med. Phys., 26 (1999), 880.
doi: 10.1118/1.598603. |
[12] |
L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo and J. M. Reinhardt, Breast mri lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system,, J. Magn. Reson. Imaging, 25 (2007), 89.
doi: 10.1002/jmri.20794. |
[13] |
S. Mirowitz, Intracranial lesion enhancement with gadolinium: T1-weighted spin-echo versus three-dimensional Fourier transform gradient-echo MR imaging,, Am. J. Neuroradiol., 20 (1992), 1554. Google Scholar |
[14] |
J. Park and E. Y. Kim, Contrast-enhanced, three-dimensional, whole-brain, black-blood imaging: Application to small brain metastases,, Magn. Reson. Med., 63 (2010), 553.
doi: 10.1002/mrm.22261. |
[15] |
D. Pham, C. Xu and J. Prince, Current Methods in medical image segmentation,, Annual Review of Biomedical Engineering, 2 (2000), 315. Google Scholar |
[16] |
M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors,, Acad. Radiol., 10 (2003), 1341. Google Scholar |
[17] |
M. G. Ranasinghe and J. M. Sheehan, Surgical management of brain metastases,, Neurosurg Focus, 22 (2007), 1.
doi: 10.3171/foc.2007.22.3.3. |
[18] |
P. D. Schellinger, H. M. Meinck and A. Thron, Diagnostic accuracy of MRI compared to CCT in patients with brain metastases,, J. Neurooncol., 44 (1999), 275. Google Scholar |
[19] |
T. Sugahara, Y. Korogi, Y. Ge, Y. Shigematsu, L. Liang, K. Yoshizumi, M. Kitajima and M. Takahashi, Contrast enhancement of intracranial lesions: Conventional T1-weighted spin-echo versus fast spin-echo MR imaging techniques,, Am. J. Neuroradiol., 20 (1999), 1554. Google Scholar |
[20] |
G. Sze, E. Milano, C. Johnson and L. Heier, Detection of brain metastases: Comparison of contrast-enhanced MR with unenhanced MR and enhanced CT,, Am. J. Neuroradiol, 11 (1990), 785. Google Scholar |
[21] |
S. Viswanath, B. N. Bloch, E. Genega, N. Rofsky, R. Lenkinski, J. Chappelow, R. Toth and A. Madabhushi, A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI,, Med. Image. Comput. Comput. Assist. Interv., 11 (2008), 662.
doi: 10.1007/978-3-540-85988-8_79. |
[22] |
P. Wang, A. DeNunzio, P. Okunieff and W. G. O'Dell, Lung metastases detection using 3d template matching,, Med. Phys., 34 (2007), 915. Google Scholar |
[23] |
T. C. Williams, W. B. DeMartini, S. C. Partridge, S. Peacock and C. D. Lehman, Breast MR imaging: Computer-aided evaluation for discriminating benign from malignant lesions,, Radiology, 244 (2007), 94. Google Scholar |
[24] |
C. Wood, Computer aided detection (CAD) for breast MRI,, Technol Cancer Res Treat, 4 (2005), 49. Google Scholar |
[25] |
A. Yezzi, S. Kichenassaym, A. Kumar, P. Olver and A. Tannenbaum, A geometric snake model for segmentation of medical imagery,, IEEE Trans. Med. Imaging., 16 (1997), 199.
doi: 10.1109/42.563665. |
[26] |
B. Zhao, G. Gamsu, M. S. Ginsberg, L. Jiang and L. H. Schwartz, Automatic detection of small lung nodules on ct utilizing a local density maximum algorithm,, J. Appl. Clin. Med. Phys., 4 (2003), 248. Google Scholar |
show all references
References:
[1] |
R. D. Ambrosini, P. Wang and W. G. O'Dell, Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching,, J. Magn. Reson. Imaging., 31 (2010), 85.
doi: 10.1002/jmri.22009. |
[2] |
T. Chan and L. Vese, Active Contours Without Edges,, IEEE Trans. Image Proc., 10 (2001), 266.
doi: 10.1109/83.902291. |
[3] |
K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,, Comput Med Imaging Graph Epub, 31 (2007), 198.
doi: 10.1016/j.compmedimag.2007.02.002. |
[4] |
K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging,, Elsevier Science Pub, (1999). Google Scholar |
[5] |
R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor,, ICGST-GVIP Journal, 9 (2009), 33. Google Scholar |
[6] |
D. Finelli, G. Hurst, R. Gullapali and E. Bellon, Improved contrast of enhancing brain lesions on postgadolinium, T1-weighted spin-echo images with use of magnetization transfer,, Radiology, 190 (1994), 553. Google Scholar |
[7] |
C. I. Henschke, D. F. Yankelevitz, I. Mateescu, D. W. Brettle, T. G. Rainey and F. S. Weingard, Neural networks for the analysis of small pulmonary nodules,, Clin Imaging., 21 (1997), 390.
doi: 10.1016/S0899-7071(97)81731-7. |
[8] |
J. Jagannathan, J. H. Sherman, G. U. Mehta GU and L. S. Chin, Radiobiology of brain metastasis: Applications in stereotactic radiosurgery,, Neurosurg Focus, 22 (2007), 1.
doi: 10.3171/foc.2007.22.3.5. |
[9] |
Y. Lee, T. Hara, H. Fujita, S. Itoh and T. Ishigaki, Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique,, IEEE Trans. Med. Imaging, 20 (2001), 595. Google Scholar |
[10] |
A. Madabhushi, M. D. Feldman, D. N. Metaxas, J. Tomaszeweski and D. Chute, Automated detection of prostatic adenocarcinoma from high-resolution ex vivo mri,, IEEE Trans. Med. Imaging, 24 (2005), 1611.
doi: 10.1109/TMI.2005.859208. |
[11] |
M. F. McNitt-Gray, E. M. Hart, N. Wyckoff, J. W. Sayre, J. G. Goldin and D. R. Aberle, A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution ct: Preliminary results,, Med. Phys., 26 (1999), 880.
doi: 10.1118/1.598603. |
[12] |
L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo and J. M. Reinhardt, Breast mri lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system,, J. Magn. Reson. Imaging, 25 (2007), 89.
doi: 10.1002/jmri.20794. |
[13] |
S. Mirowitz, Intracranial lesion enhancement with gadolinium: T1-weighted spin-echo versus three-dimensional Fourier transform gradient-echo MR imaging,, Am. J. Neuroradiol., 20 (1992), 1554. Google Scholar |
[14] |
J. Park and E. Y. Kim, Contrast-enhanced, three-dimensional, whole-brain, black-blood imaging: Application to small brain metastases,, Magn. Reson. Med., 63 (2010), 553.
doi: 10.1002/mrm.22261. |
[15] |
D. Pham, C. Xu and J. Prince, Current Methods in medical image segmentation,, Annual Review of Biomedical Engineering, 2 (2000), 315. Google Scholar |
[16] |
M. Prastawa, E. Bullitt, N. Moon, K. Van Leemput and G. Gerig, Automatic brain tumor segmentation by subject specific modification of atlas priors,, Acad. Radiol., 10 (2003), 1341. Google Scholar |
[17] |
M. G. Ranasinghe and J. M. Sheehan, Surgical management of brain metastases,, Neurosurg Focus, 22 (2007), 1.
doi: 10.3171/foc.2007.22.3.3. |
[18] |
P. D. Schellinger, H. M. Meinck and A. Thron, Diagnostic accuracy of MRI compared to CCT in patients with brain metastases,, J. Neurooncol., 44 (1999), 275. Google Scholar |
[19] |
T. Sugahara, Y. Korogi, Y. Ge, Y. Shigematsu, L. Liang, K. Yoshizumi, M. Kitajima and M. Takahashi, Contrast enhancement of intracranial lesions: Conventional T1-weighted spin-echo versus fast spin-echo MR imaging techniques,, Am. J. Neuroradiol., 20 (1999), 1554. Google Scholar |
[20] |
G. Sze, E. Milano, C. Johnson and L. Heier, Detection of brain metastases: Comparison of contrast-enhanced MR with unenhanced MR and enhanced CT,, Am. J. Neuroradiol, 11 (1990), 785. Google Scholar |
[21] |
S. Viswanath, B. N. Bloch, E. Genega, N. Rofsky, R. Lenkinski, J. Chappelow, R. Toth and A. Madabhushi, A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI,, Med. Image. Comput. Comput. Assist. Interv., 11 (2008), 662.
doi: 10.1007/978-3-540-85988-8_79. |
[22] |
P. Wang, A. DeNunzio, P. Okunieff and W. G. O'Dell, Lung metastases detection using 3d template matching,, Med. Phys., 34 (2007), 915. Google Scholar |
[23] |
T. C. Williams, W. B. DeMartini, S. C. Partridge, S. Peacock and C. D. Lehman, Breast MR imaging: Computer-aided evaluation for discriminating benign from malignant lesions,, Radiology, 244 (2007), 94. Google Scholar |
[24] |
C. Wood, Computer aided detection (CAD) for breast MRI,, Technol Cancer Res Treat, 4 (2005), 49. Google Scholar |
[25] |
A. Yezzi, S. Kichenassaym, A. Kumar, P. Olver and A. Tannenbaum, A geometric snake model for segmentation of medical imagery,, IEEE Trans. Med. Imaging., 16 (1997), 199.
doi: 10.1109/42.563665. |
[26] |
B. Zhao, G. Gamsu, M. S. Ginsberg, L. Jiang and L. H. Schwartz, Automatic detection of small lung nodules on ct utilizing a local density maximum algorithm,, J. Appl. Clin. Med. Phys., 4 (2003), 248. Google Scholar |
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