Abstract
Recently, high resolution 3 Tesla (T) Dynamic Contrast-Enhanced MRI (DCE-MRI) of the prostate has emerged as a promising modality for detecting prostate cancer (CaP). Computer-aided diagnosis (CAD) schemes for DCE-MRI data have thus far been primarily developed for breast cancer and typically involve model fitting of dynamic intensity changes as a function of contrast agent uptake by the lesion. Comparatively there is relatively little work in developing CAD schemes for prostate DCE-MRI. In this paper, we present a novel unsupervised detection scheme for CaP from 3 T DCE-MRI which comprises 3 distinct steps. First, a multi-attribute active shape model is used to automatically segment the prostate boundary from 3 T in vivo MR imagery. A robust multimodal registration scheme is then used to non-linearly align corresponding whole mount histological and DCE-MRI sections from prostatectomy specimens to determine the spatial extent of CaP. Non-linear dimensionality reduction schemes such as locally linear embedding (LLE) have been previously shown to be useful in projecting such high dimensional biomedical data into a lower dimensional subspace while preserving the non-linear geometry of the data manifold. DCE-MRI data is embedded via LLE and then classified via unsupervised consensus clustering to identify distinct classes. Quantitative evaluation on 21 histology-MRI slice pairs against registered CaP ground truth estimates yielded a maximum CaP detection accuracy of 77.20% while the popular three time point (3TP) scheme yielded an accuracy of 67.37%.
Chapter PDF
References
Padhani, A., Gapinski, C., et al.: Dynamic Contrast Enhanced MRI of Prostate Cancer: Correlation with Morphology and Tumour Stage, Histological Grade and PSA. Clinical Radiology 55(2), 99–109 (2000)
Degani, H., Gusis, V., et al.: Mapping pathophysiological features of breast tumours by MRI at high spatial resolution. Nature Medicine 3(7), 780–782 (1997)
Vos, P., Hambrock, T., et al.: Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI. Medical Physics 35(3), 888–899 (2008)
Madabhushi, A., Udupa, J.: New Methods of MR Image Intensity Standardization via Generalized Scale. Medical Physics 33(9), 3426–3434 (2006)
Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)
Varini, C., Degenhard, A., et al.: Visual exploratory analysis of DCE-MRI data in breast cancer by dimensional data reduction: a comparative study. Biomedical Signal Processing and Control 1(1), 56–63 (2006)
Toth, R., Tiwari, P., et al.: A multi-modal prostate segmentation scheme by combining spectral clustering and active shape models. In: SPIE Medical Imaging, pp. 69144S1–69144S12 (2008)
Chappelow, J., Madabhushi, A., et al.: A combined feature ensemble based mutual information scheme for robust inter-modal, inter-protocol image registration. In: International Symposium on Biomedical Imaging, pp. 644–647 (2007)
Fred, A., Jain, A.: Combining Multiple Clusterings Using Evidence Accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 835–850 (2005)
Venna, J., Kaski, S.: Local multidimensional scaling. Neural Networks 19(6), 889–899 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Viswanath, S. et al. (2008). A Comprehensive Segmentation, Registration, and Cancer Detection Scheme on 3 Tesla In Vivo Prostate DCE-MRI. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_79
Download citation
DOI: https://doi.org/10.1007/978-3-540-85988-8_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85987-1
Online ISBN: 978-3-540-85988-8
eBook Packages: Computer ScienceComputer Science (R0)