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Optimized prostate biopsy via a statistical atlas of cancer spatial distribution

Dinggang Shen, Zhiqiang Lao, Jianchao Zeng, Wei Zhang, Isabel A. Sesterhenn, Leon Sun, Judd W. Moul, Edward H. Herskovits, Gabor Fichtinger, Christos Davatzikos
2004 Medical Image Analysis  
A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy.  ...  Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer.  ...  Acknowledgements This work was supported in part by a grant from the National Science Foundation to the Engineering Research Center for Computer Integrated Surgical Systems and Technology. J.  ... 
doi:10.1016/ pmid:15063863 fatcat:7ezjynltevcgto3mdoabsise54

Targeted Prostate Biopsy Using Statistical Image Analysis

Yiqiang Zhan, Dinggang Shen, Jianchao Zeng, Leon Sun, Gabor Fichtinger, Judd Moul, Christos Davatzikos
2007 IEEE Transactions on Medical Imaging  
First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen.  ...  Index Terms-Biopsy optimization, prostate cancer, spatial normalization, statistical image analysis.  ...  ACKNOWLEDGMENT The authors would like to acknowledge the DoD Center for Prostate Disease Research for providing the histological sections.  ... 
doi:10.1109/tmi.2006.891497 pmid:17679329 fatcat:dimtuimb6vhi3npmbnu2ccsoyy

Sampling the spatial patterns of cancer: Optimized biopsy procedures for estimating prostate cancer volume and Gleason Score

Yangming Ou, Dinggang Shen, Jianchao Zeng, Leon Sun, Judd Moul, Christos Davatzikos
2009 Medical Image Analysis  
First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths.  ...  Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation.  ...  Mitch Schnall at the Hospital of the University of Pennsylvania (HUP) for discussions on clinical significance of prostate cancer.  ... 
doi:10.1016/ pmid:19524478 pmcid:PMC2748333 fatcat:s7kuqjun7zg5bggrlcsnlpzocm

Prostatome: A combined anatomical and disease based MRI atlas of the prostate

Mirabela Rusu, B. Nicolas Bloch, Carl C. Jaffe, Elizabeth M. Genega, Robert E. Lenkinski, Neil M. Rofsky, Ernest Feleppa, Anant Madabhushi
2014 Medical Physics (Lancaster)  
These techniques, may be applied on a large sample size data set to create a fully developed prostatome that could serve as a spatial prior for targeted biopsies by urologists.  ...  As might have been anticipated from our limited cohort of biopsy confirmed cancers, the disease atlas showed that most of the tumor extent was limited to the peripheral zone.  ...  Cancer spatial distribution atlas A ca coupled with the statistical shape atlas of the prostate anatomic substructures: (a)-(c) A cg , A pz , A pr ; (d)-(f) A carelative to statistical shape of the prostate  ... 
doi:10.1118/1.4881515 pmid:24989400 pmcid:PMC4187363 fatcat:4fbjpbss2jg43d6yfxedygzn5e

Application of statistical cancer atlas for 3D biopsy

Ramkrishnan Narayanan, Dinggang Shen, Christos Davatzikos, E. David Crawford, Albaha Barqawi, Priya Werahera, Dinesh Kumar, Jasjit S. Suri, Jaakko T. Astola, Karen O. Egiazarian, Edward R. Dougherty
2008 Image Processing: Algorithms and Systems VI  
Recently a statistical cancer atlas of the prostate was demonstrated along with an optimal biopsy scheme achieving a high detection rate.  ...  While the atlas surface can be registered to a pre-segmented subject prostate surface or instead used to perform segmentation of the capsule via optimization of shape parameters to segment the subject  ...  In Opell et al 8 , authors develop a spatial distribution map of cancers in the prostate concluding that the cancers are more commonly found in the posterior half, apical and mid regions of the prostate  ... 
doi:10.1117/12.766633 dblp:conf/ipas/NarayananSDCBWKS08 fatcat:43b6oofbbjdcvlxnvjtozpubpu

Three-Dimensional Sonography With Needle Tracking

Feimo Shen, Katsuto Shinohara, Dinesh Kumar, Animesh Khemka, Anne R. Simoneau, Priya N. Werahera, Lu Li, Yujun Guo, Ramkrishnan Narayanan, Liyang Wei, Al Barqawi, E. David Crawford (+2 others)
2008 Journal of ultrasound in medicine  
Additionally, when fusing together different imaging modalities and cancer probability maps obtained from a population of interest, physicians can potentially place biopsy needles and other interventional  ...  Image-guided prostate biopsy has become routine in medical diagnosis.  ...  Opell et al 34 developed a spatial distribution map of cancers in the prostate: They concluded that the cancers were more commonly found in the posterior half and the apical and mid regions of the prostate  ... 
doi:10.7863/jum.2008.27.6.895 pmid:18499849 pmcid:PMC3402711 fatcat:x6rhjeyifjaehklstoosxh6e7m

Statistical 3D prostate imaging atlas construction via anatomically constrained registration

Mirabela Rusu, B. Nicolas Bloch, Carl C. Jaffe, Neil M. Rofsky, Elizabeth M. Genega, Ernest Feleppa, Robert E. Lenkinski, Anant Madabhushi, David R. Haynor, Sebastien Ourselin
2013 Medical Imaging 2013: Image Processing  
Despite the high incidence of prostate cancer, an imaging atlas focused on different anatomic structures of the prostate, i.e. an anatomic atlas, has yet to be constructed.  ...  Such distributions are useful for guiding biopsies toward regions of higher cancer likelihood and understanding imaging profiles for disease extent in vivo.  ...  ACKNOWLEDGEMENTS This work was made possible by grants from the National Institute of Health (R01CA136535, R01CA140772, R43EB015199, R21CA167811), National Science Foundation (IIP-1248316), and the QED  ... 
doi:10.1117/12.2006941 pmid:24392203 pmcid:PMC3877325 dblp:conf/miip/RusuBJRGFLM13 fatcat:vpe3xhhwf5cr5jgdnam56vqoyy

A structural-functional MRI-based disease atlas: application to computer-aided-diagnosis of prostate cancer

G. Xiao, B. Bloch, J. Chappelow, E. Genega, N. Rofsky, R. Lenkinski, A. Madabhushi, Benoit M. Dawant, David R. Haynor
2010 Medical Imaging 2010: Image Processing  
Most previous work on construction of anatomical atlases has focused on deriving a population-based atlas for the purpose of deriving the spatial statistics.  ...  spatial atlas which captures the geographical proclivity of the disease, and (c) feature extraction and the construction of the data-driven multi-protocol MRI based prostate cancer atlas.  ...  Coulter Foundation, New Jersey Commission on Cancer Research, National Cancer Institute (R01CA136535-01, R21CA127186-01, R03CA128081-01), the Cancer Institute of New Jersey, and Bioimagene Inc.  ... 
doi:10.1117/12.845554 dblp:conf/miip/XiaoBCGRLM10 fatcat:iw6r2n7q2nhvrcfvejfoh74x6e

Methodology to study the three-dimensional spatial distribution of prostate cancer and their dependence on clinical parameters

Kristians Diaz Rojas, Maria L. Montero, Jorge Yao, Edward Messing, Anees Fazili, Jean Joseph, Yangming Ou, Deborah J. Rubens, Kevin J. Parker, Christos Davatzikos, Benjamin Castaneda
2015 Journal of Medical Imaging  
Spatial cancer distribution is assessed by counting the number of tumor occurrences among all glands in a given position of the 3-D registered atlas.  ...  A methodology to study the relationship between clinical variables [e.g., prostate specific antigen (PSA) or Gleason score] and cancer spatial distribution is described.  ...  Statistical Analysis Once all of the prostate glands have been registered into a unique atlas model, the spatial distribution is developed by counting the number of occurrences in a given position in the  ... 
doi:10.1117/1.jmi.2.3.037502 pmid:26236756 pmcid:PMC4518233 fatcat:i2hq7ywf6zhu3j374kgpfzkgcm

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

Yanrong Guo, Yaozong Gao, Yeqin Shao, True Price, Aytekin Oto, Dinggang Shen
2014 Medical Physics (Lancaster)  
In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model non-Gaussian distributions  ...  Guo et al.: Deformable segmentation of prostate MRI via DDD learning 072303-2 Conclusions: A new magnetic resonance image prostate segmentation method is proposed based on the combination of deformable  ...  prostate core biopsy 2, 3 has become a preferred alternative for prostate cancer detection. 4 To place the needle accurately during biopsy, accurate detection, localization, and characterization of  ... 
doi:10.1118/1.4884224 pmid:24989402 pmcid:PMC4105964 fatcat:ofyrc5vyrjbkdop5eayhrmammu

Prostate cancer radiomics and the promise of radiogenomics

Radka Stoyanova, Mandeep Takhar, Yohann Tschudi, John C Ford, Gabriel Solórzano, Nicholas Erho, Yoganand Balagurunathan, Sanoj Punnen, Elai Davicioni, Robert J Gillies, Alan Pollack
2016 Translational Cancer Research  
a more personalized approach to prostate cancer management.  ...  In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure.  ...  Efforts are underway for automation of this process by utilizing a prostate atlas.  ... 
pmid:29188191 pmcid:PMC5703221 fatcat:3xig5jwxbfhmxhexb2b4ivijb4

Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review

Zia Khan, Norashikin Yahya, Khaled Alsaih, Mohammed Isam Al-Hiyali, Fabrice Meriaudeau
2021 IEEE Access  
One of every 1174 five cancer diagnoses is a prostate cancer [197] Traditionally, 1175 prostate cancer is diagnosed by biopsy but there is evidence 1176 of an unequivocal benefit of multiparametric MRI-targeted  ...  1177 biopsies for more systematic biopsies in diagnosis of prostate 1178 cancer. 1179 As the biopsy is planned, prostate MRI scanning helps in 1180 locating the target area, and therefore reduces the  ... 
doi:10.1109/access.2021.3090825 fatcat:l2xe2tdwk5b6ldn7axvzbp5a5a

Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

Leonardo Rundo, Carmelo Militello, Giorgio Russo, Antonio Garufi, Salvatore Vitabile, Maria Gilardi, Giancarlo Mauri
2017 Information  
Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer.  ...  This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/info8020049 fatcat:3petzh66ezhf7fukdtkkd2sykm

Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework

Audrey G. Chung, Farzad Khalvati, Mohammad Javad Shafiee, Masoom A. Haider, Alexander Wong
2015 IEEE Access  
a plethora of mineable data, which can be used for both detection and prognosis of prostate cancer.  ...  Motivated by this, we present a novel approach for automatic prostate cancer detection using a radiomics-driven conditional random field (RD-CRF) framework.  ...  In a two-stage computer-aided prostate cancer detection system, Litjens et al. detected initial candidates via multi-atlas-based prostate segmentation using a selective and iterative method for performance  ... 
doi:10.1109/access.2015.2502220 fatcat:u2pwqteiinad7dfdknsi3ykvwi

Prostate biopsy tracking with deformation estimation

Michael Baumann, Pierre Mozer, Vincent Daanen, Jocelyne Troccaz
2012 Medical Image Analysis  
Transrectal biopsies under 2D ultrasound (US) control are the current clinical standard for prostate cancer diagnosis.  ...  Tracking systems for prostate biopsies make it possible to generate biopsy distribution maps for intra- and post-interventional quality control and 3D visualisation of histological results for diagnosis  ...  We would furthermore like to thank Sébastien Martin, PhD, TIMC laboratory, Grenoble, France, for making his statistical prostate shape and deformation priors available for the segmentation module of the  ... 
doi:10.1016/ pmid:21705263 fatcat:x3vh5id5e5bjnlbdtyu6vptrca
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