@misc{Viswanath2008,
   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%. © 2008 Springer-Verlag Berlin Heidelberg.},
   author = {Satish E. Viswanath and B.N. Bloch and E.M. Genega and N.M. Rofsky and R.E. Lenkinski and J.C. Chappelow and R. Toth and A. Madabhushi},
   doi = {10.1007/978-3-540-85988-8_79},
   isbn = {354085987X},
   issn = {03029743},
   issue = {PART 1},
   journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
   title = {A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI},
   volume = {5241 LNCS},
   year = {2008},
}

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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%. © 2008 Springer-Verlag Berlin Heidelberg.","author":[{"firstnames":["Satish","E."],"propositions":[],"lastnames":["Viswanath"],"suffixes":[]},{"firstnames":["B.N."],"propositions":[],"lastnames":["Bloch"],"suffixes":[]},{"firstnames":["E.M."],"propositions":[],"lastnames":["Genega"],"suffixes":[]},{"firstnames":["N.M."],"propositions":[],"lastnames":["Rofsky"],"suffixes":[]},{"firstnames":["R.E."],"propositions":[],"lastnames":["Lenkinski"],"suffixes":[]},{"firstnames":["J.C."],"propositions":[],"lastnames":["Chappelow"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Toth"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Madabhushi"],"suffixes":[]}],"doi":"10.1007/978-3-540-85988-8_79","isbn":"354085987X","issn":"03029743","issue":"PART 1","journal":"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","title":"A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI","volume":"5241 LNCS","year":"2008","bibtex":"@misc{Viswanath2008,\n 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. 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