American Institute of Mathematical Sciences

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May  2014, 8(2): 491-505. doi: 10.3934/ipi.2014.8.491

A new computer-aided method for detecting brain metastases on contrast-enhanced MR images

Hyeuknam Kwon 1, , Yoon Mo Jung 1, , Jaeseok Park 2, and  Jin Keun Seo 1,

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

Received  February 2012 Revised  February 2013 Published  May 2014

This paper presents a new computer-aided method for detection of brain metastases at early-stage (diameter less than $6$mm) on MR images. The proposed detection method has a high level of sensitivity with a relatively low number of false-positives. The strong detection capability of the method is possible due to a size filtering function that sorts out metastases based on the geometry and size. In experiments, we used whole-brain MR data acquired with a contrast-enhanced black-blood type MR imaging technique, which enables distinction of brain metastases from blood vessels. The proposed method performed highly in analysis of the results of experimental MR data and numerical simulation. Because the proposed method has unique features, it could be used in combination with a complementary pre-existing technique.
Keywords: MRI., filtering function, Computer-aided detection, brain metastases, segmentation.
Mathematics Subject Classification: 00A69, 00A66, 94A0.
Citation: Hyeuknam Kwon, Yoon Mo Jung, Jaeseok Park, Jin Keun Seo. A new computer-aided method for detecting brain metastases on contrast-enhanced MR images. Inverse Problems and Imaging, 2014, 8 (2) : 491-505. doi: 10.3934/ipi.2014.8.491
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), 85-93. doi: 10.1002/jmri.22009.

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K. Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential, Comput Med Imaging Graph Epub, 31 (2007), 198-211. doi: 10.1016/j.compmedimag.2007.02.002.

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K. Doi, M. L. Giger and K. Doi, Computer-Aided Diagnosis in Medical Imaging, Elsevier Science Pub, 1999.

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R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor, ICGST-GVIP Journal, 9 (2009), 33-40.

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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), 553-559.

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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), 390-399. doi: 10.1016/S0899-7071(97)81731-7.

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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), 1-5. doi: 10.3171/foc.2007.22.3.5.

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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), 595-604.

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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), 1611-1625. doi: 10.1109/TMI.2005.859208.

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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), 880-888. 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), 89-95. 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), 1554-1559.

[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), 553-561. 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), 315-337.

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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), 1341-1348.

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M. G. Ranasinghe and J. M. Sheehan, Surgical management of brain metastases, Neurosurg Focus, 22 (2007), 1-7. 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), 275-281.

[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), 1554-1559.

[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), 785-791.

[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), 662-669. 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), 915-922.

[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), 94-103.

[24]

C. Wood, Computer aided detection (CAD) for breast MRI, Technol Cancer Res Treat, 4 (2005), 49-53.

[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), 199-209. 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), 248-260.

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), 85-93. doi: 10.1002/jmri.22009.

[2]

T. Chan and L. Vese, Active Contours Without Edges, IEEE Trans. Image Proc., 10 (2001), 266-277. 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), 198-211. 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.

[5]

R. Dubey, M. Hanmandlu, S. Gupta and S. Gupta, emi-automatic Segmentation of MRI Brain Tumor, ICGST-GVIP Journal, 9 (2009), 33-40.

[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), 553-559.

[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), 390-399. 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), 1-5. 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), 595-604.

[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), 1611-1625. 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), 880-888. 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), 89-95. 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), 1554-1559.

[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), 553-561. 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), 315-337.

[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), 1341-1348.

[17]

M. G. Ranasinghe and J. M. Sheehan, Surgical management of brain metastases, Neurosurg Focus, 22 (2007), 1-7. 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), 275-281.

[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), 1554-1559.

[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), 785-791.

[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), 662-669. 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), 915-922.

[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), 94-103.

[24]

C. Wood, Computer aided detection (CAD) for breast MRI, Technol Cancer Res Treat, 4 (2005), 49-53.

[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), 199-209. 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), 248-260.

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