Abstract
To find an effective way to quantitatively analyze the thickness variation of human bladder wall under different states, in this paper, we proposed a novel pipeline for thickness measurement, analysis, and mapping of bladder wall based on T2-weighted MRI images. The pipeline includes major steps of data acquisition, automatic segmentation of bladder wall, 3D thickness calculation, thickness normalization, and standardized bladder shape mapping. Based on the proposed pipeline, 20 datasets including 10 patients and 10 volunteers were used to explore the distribution pattern of wall thickness and find the difference between cancerous tissue and normal bladder wall. The results demonstrated the potential of wall thickness as a good indicator of bladder abnormalities, indicating its possible use in lesion detection on the bladder wall.
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References
American Cancer Society: Cancer facts and figures 2013, pp. 4–10. Atlanta, American Cancer Society (2013)
National Comprehensive Cancer Network: Bladder cancer V.1.2013, pp. 1–3. National Comprehensive Cancer Network, Fort Washington (2013). (MS)
Beer, A., Saar, B., Zantl, N., et al.: MR cystography for bladder tumor detection. Eur. Radiol. 14, 2311–2319 (2004)
Suleyman, E., Yekeler, E., Dursun, M., et al.: Bladder tumors: virtual MR cystoscopy. Abdom. Imaging 31(4), 483–489 (2006)
Jaume, S., Ferrant, M., Macq, B., Hoyte, L., Fielding, J.R., Schreyer, A., Kikinis, R., Warfield, S.K.: Tumor detection in the bladder wall with a measurement of abnormal thickness in CT scans. IEEE Trans. Biomed. Eng. 50, 383–390 (2003)
Fielding, J.R., Hoyte, L., Okon, S.A., Schreyer, A., Lee, J., Zou, K.H., Warfield, S., Richie, J.P., Loughlin, K.R., O’Leary, M.P., Doyle, C.J., Kikinis, R.: Tumor detection by virtual cystoscopy with color mapping of bladder wall thickness. J. Urol. 167, 559–562 (2002)
Zhu, H., Duan, C., Jiang, R., Li, L., Fan, Y., Yu, X., Zeng, W., Gu, X., Liang, Z.: Computer-aided detection of bladder tumors based on the thickness mapping of bladder wall in MR images. Proc. SPIE Med. Imaging 7623, 76234H1–76234H8 (2010)
Duan, C., Liang, Z., Bao, S., et al.: A coupled level set framework for bladder wall segmentation with application to MR cystography. IEEE Trans. Med. Imaging 29(3), 903–915 (2010)
Qin, X., Liu, Y., Lu, H., Li, X., Yan, P.: Coupled directional level set for MR image segmentation. In: IEEE ICMLA, vol. 39 (2012)
Liu, Y., Li, Y.J., Luo, E.P., Lu, H.B., Yin, H.: Cortical thinning in patients with recent onset post-traumatic stress disorder after a single prolonged trauma exposure. PLoS ONE 7(6), e39025 (2012). doi:10.1371/journal.pone.0039025
Zhao, Y., Liu, Y., Feng, J., Lu, H.: Parametric mapping model for bladder using free-form deformation. In: Conference Record of 2013 IEEE NSS/MIC/RTSD, Seoul, Oct 29–Nov 2 (2013)
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Zhang, X., Liu, Y., Xiao, D., Zhang, G., Liao, Q., Lu, H. (2014). MRI-Based Thickness Analysis of Bladder Cancer: A Pilot Study. In: Yoshida, H., Näppi, J., Saini, S. (eds) Abdominal Imaging. Computational and Clinical Applications. ABD-MICCAI 2014. Lecture Notes in Computer Science(), vol 8676. Springer, Cham. https://doi.org/10.1007/978-3-319-13692-9_24
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DOI: https://doi.org/10.1007/978-3-319-13692-9_24
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