@book{
 title = {A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI},
 type = {book},
 year = {2008},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 volume = {5241 LNCS},
 issue = {PART 1},
 id = {eda6a86a-5512-31d9-9dc9-b74d67c958ff},
 created = {2023-10-25T08:56:41.079Z},
 file_attached = {false},
 profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},
 last_modified = {2023-10-25T08:56:41.079Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 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.},
 bibtype = {book},
 author = {Viswanath, S. and Bloch, B.N. and Genega, E. and Rofsky, N. and Lenkinski, R. and Chappelow, J. and Toth, R. and Madabhushi, A.},
 doi = {10.1007/978-3-540-85988-8_79}
}

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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.","bibtype":"book","author":"Viswanath, S. and Bloch, B.N. and Genega, E. and Rofsky, N. and Lenkinski, R. and Chappelow, J. and Toth, R. and Madabhushi, A.","doi":"10.1007/978-3-540-85988-8_79","bibtex":"@book{\n title = {A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI},\n type = {book},\n year = {2008},\n source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},\n volume = {5241 LNCS},\n issue = {PART 1},\n id = {eda6a86a-5512-31d9-9dc9-b74d67c958ff},\n created = {2023-10-25T08:56:41.079Z},\n file_attached = {false},\n profile_id = {eaba325f-653b-3ee2-b960-0abd5146933e},\n last_modified = {2023-10-25T08:56:41.079Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\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. 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.},\n bibtype = {book},\n author = {Viswanath, S. and Bloch, B.N. and Genega, E. and Rofsky, N. and Lenkinski, R. and Chappelow, J. and Toth, R. and Madabhushi, A.},\n doi = {10.1007/978-3-540-85988-8_79}\n}","author_short":["Viswanath, S.","Bloch, B.","Genega, E.","Rofsky, N.","Lenkinski, R.","Chappelow, J.","Toth, R.","Madabhushi, A."],"biburl":"https://bibbase.org/service/mendeley/eaba325f-653b-3ee2-b960-0abd5146933e","bibbaseid":"viswanath-bloch-genega-rofsky-lenkinski-chappelow-toth-madabhushi-acomprehensivesegmentationregistrationandcancerdetectionschemeon3teslainvivoprostatedcemri-2008","role":"author","urls":{},"metadata":{"authorlinks":{"viswanath, s":"https://bibbase.org/service/mendeley/eaba325f-653b-3ee2-b960-0abd5146933e","viswanath, s":"https://bibbase.org/show?msg=embed&bib=https://dblp.org/pid/75/5518.bib"}},"downloads":0},"bibtype":"book","creationDate":"2020-04-20T04:32:18.787Z","downloads":0,"keywords":[],"search_terms":["comprehensive","segmentation","registration","cancer","detection","scheme","tesla","vivo","prostate","dce","mri","viswanath","bloch","genega","rofsky","lenkinski","chappelow","toth","madabhushi"],"title":"A comprehensive segmentation, registration, and cancer detection scheme on 3 tesla in vivo prostate DCE-MRI","year":2008,"biburl":"https://bibbase.org/service/mendeley/eaba325f-653b-3ee2-b960-0abd5146933e","dataSources":["EqsrWP7MGiqJ6MAPy","ya2CyA73rpZseyrZ8","MFTLffJ4nzQec8k8n","4uxW7RrvLiakZtfwv","hFzKqXpEPLMveFEmo","QFDqFYgwtDZoyYk6v","2252seNhipfTmjEBQ"]}