@misc{Viswanath2009,
   abstract = {Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.©2009 SPIE.},
   author = {Satish E. Viswanath and B.N. Bloch and M.A. Rosen and J.C. Chappelow and R. Toth and N.M. Rofsky and R.E. Lenkinski and E.M. Genega and A. Kalyanpur and A. Madabhushi},
   doi = {10.1117/12.811899},
   isbn = {9780819475114},
   issn = {16057422},
   journal = {Progress in Biomedical Optics and Imaging - Proceedings of SPIE},
   keywords = {3 Tesla,Bagging,CAD,DCE-MRI,Data fusion,Decision fusion,Decision trees,Multimodal integration,Non-rigid registration,Prostate cancer,Random forests,Segmentation,Supervised learning,T2-w MRI},
   title = {Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 tesla MRI},
   volume = {7260},
   year = {2009},
}

{"_id":"S5HQ3tLyGmgAf7Fmc","bibbaseid":"viswanath-bloch-rosen-chappelow-toth-rofsky-lenkinski-genega-etal-integratingstructuralandfunctionalimagingforcomputerassisteddetectionofprostatecanceronmultiprotocolinvivo3teslamri-2009","authorIDs":["4Bygm8ju5EJL5ymrQ","6QhxQnZANogYLvQK3","95yz33vzsQF4xkftF","9XZAWfqroLnhv8Yvi","FNZqkqoLDpk7PuWdD","MFZedjHxD2knXtEDJ","QcPeEw2JciXtbZFYv","h2B3dsR3oRk42DaEg","ikoetnssERNBFB49Z","kEjtqmTXpJD7idBiM","mKXojsM3x8movswn3","mTxR5EmKaPZT4FPRw","wqGyw5WWC92w63FBr","zmtHQr2MyDkT4KaPS","zy5Yt2FBfMrjEQLRW"],"author_short":["Viswanath, S. E.","Bloch, B.","Rosen, M.","Chappelow, J.","Toth, R.","Rofsky, N.","Lenkinski, R.","Genega, E.","Kalyanpur, A.","Madabhushi, A."],"bibdata":{"bibtype":"misc","type":"misc","abstract":"Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.©2009 SPIE.","author":[{"firstnames":["Satish","E."],"propositions":[],"lastnames":["Viswanath"],"suffixes":[]},{"firstnames":["B.N."],"propositions":[],"lastnames":["Bloch"],"suffixes":[]},{"firstnames":["M.A."],"propositions":[],"lastnames":["Rosen"],"suffixes":[]},{"firstnames":["J.C."],"propositions":[],"lastnames":["Chappelow"],"suffixes":[]},{"firstnames":["R."],"propositions":[],"lastnames":["Toth"],"suffixes":[]},{"firstnames":["N.M."],"propositions":[],"lastnames":["Rofsky"],"suffixes":[]},{"firstnames":["R.E."],"propositions":[],"lastnames":["Lenkinski"],"suffixes":[]},{"firstnames":["E.M."],"propositions":[],"lastnames":["Genega"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Kalyanpur"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Madabhushi"],"suffixes":[]}],"doi":"10.1117/12.811899","isbn":"9780819475114","issn":"16057422","journal":"Progress in Biomedical Optics and Imaging - Proceedings of SPIE","keywords":"3 Tesla,Bagging,CAD,DCE-MRI,Data fusion,Decision fusion,Decision trees,Multimodal integration,Non-rigid registration,Prostate cancer,Random forests,Segmentation,Supervised learning,T2-w MRI","title":"Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3 tesla MRI","volume":"7260","year":"2009","bibtex":"@misc{Viswanath2009,\n abstract = {Screening and detection of prostate cancer (CaP) currently lacks an image-based protocol which is reflected in the high false negative rates currently associated with blinded sextant biopsies. Multi-protocol magnetic resonance imaging (MRI) offers high resolution functional and structural data about internal body structures (such as the prostate). In this paper we present a novel comprehensive computer-aided scheme for CaP detection from high resolution in vivo multi-protocol MRI by integrating functional and structural information obtained via dynamic-contrast enhanced (DCE) and T2-weighted (T2-w) MRI, respectively. Our scheme is fully-automated and comprises (a) prostate segmentation, (b) multimodal image registration, and (c) data representation and multi-classifier modules for information fusion. Following prostate boundary segmentation via an improved active shape model, the DCE/T2-w protocols and the T2-w/ex vivo histological prostatectomy specimens are brought into alignment via a deformable, multi-attribute registration scheme. T2-w/histology alignment allows for the mapping of true CaP extent onto the in vivo MRI, which is used for training and evaluation of a multi-protocol MRI CaP classifier. The meta-classifier used is a random forest constructed by bagging multiple decision tree classifiers, each trained individually on T2-w structural, textural and DCE functional attributes. 3-fold classifier cross validation was performed using a set of 18 images derived from 6 patient datasets on a per-pixel basis. Our results show that the results of CaP detection obtained from integration of T2-w structural textural data and DCE functional data (area under the ROC curve of 0.815) significantly outperforms detection based on either of the individual modalities (0.704 (T2-w) and 0.682 (DCE)). It was also found that a meta-classifier trained directly on integrated T2-w and DCE data (data-level integration) significantly outperformed a decision-level meta-classifier, constructed by combining the classifier outputs from the individual T2-w and DCE channels.©2009 SPIE.},\n author = {Satish E. Viswanath and B.N. Bloch and M.A. Rosen and J.C. Chappelow and R. Toth and N.M. Rofsky and R.E. Lenkinski and E.M. Genega and A. Kalyanpur and A. 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