Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490333
Huanxiang Lu, M. Reyes, Amira Serijovic, S. Weber, Y. Sakurai, H. Yamagata, P. Cattin
In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.
{"title":"Multi-modal diffeomorphic demons registration based on point-wise mutual information","authors":"Huanxiang Lu, M. Reyes, Amira Serijovic, S. Weber, Y. Sakurai, H. Yamagata, P. Cattin","doi":"10.1109/ISBI.2010.5490333","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490333","url":null,"abstract":"In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129801983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490246
Y. Rathi, J. Malcolm, S. Bouix, R. McCarley, L. Seidman, J. Goldstein, C. Westin, M. Shenton
We describe a probabilistic technique for separating two populations whereby analysis is performed on affine-invariant representations of each patient. The method begins by converting each voxel from a high-dimensional diffusion weighted signal to a low-dimensional diffusion tensor representation. Three orthogonal measures that capture different aspects of the local tissue are derived from the tensor representation to form a feature vector. From these feature vectors, we form a probabilistic representation of each patient. This representation is affine invariant, thus obviating the need for registration of the images. We then use a Parzen window classifier to estimate the likelihood of a new patient belonging to either population. To demonstrate the technique, we apply it to the analysis of 22 first-episode schizophrenic patients and 20 normal control subjects. With leave-many-out cross validation, we find a detection rate of 90.91% (10% false positives).
{"title":"Disease classification: A probabilistic approach","authors":"Y. Rathi, J. Malcolm, S. Bouix, R. McCarley, L. Seidman, J. Goldstein, C. Westin, M. Shenton","doi":"10.1109/ISBI.2010.5490246","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490246","url":null,"abstract":"We describe a probabilistic technique for separating two populations whereby analysis is performed on affine-invariant representations of each patient. The method begins by converting each voxel from a high-dimensional diffusion weighted signal to a low-dimensional diffusion tensor representation. Three orthogonal measures that capture different aspects of the local tissue are derived from the tensor representation to form a feature vector. From these feature vectors, we form a probabilistic representation of each patient. This representation is affine invariant, thus obviating the need for registration of the images. We then use a Parzen window classifier to estimate the likelihood of a new patient belonging to either population. To demonstrate the technique, we apply it to the analysis of 22 first-episode schizophrenic patients and 20 normal control subjects. With leave-many-out cross validation, we find a detection rate of 90.91% (10% false positives).","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"55 32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490171
D. Zhang, L. Risser, C. Metz, L. Neefjes, N. Mollet, W. Niessen, D. Rueckert
In this paper we present a method for coronary artery motion tracking in 4D cardiac CT data sets. The algorithm allows the automatic construction of a 4D coronary motion model from pre-operative CT which can be used for guiding totally-endoscopic coronary artery bypass surgery (TECAB). The proposed approach is based on two steps: In the first step, the coronary arteries are extracted in the end-diastolic time frame using a minimal cost path approach. To achieve this, the start and end points of the coronaries are identified interactively and the minimal cost path between the start and end points is computed using the A* graph algorithm. In the second stage the coronaries are tracked automatically through all other phases of the cardiac cycle. This is achieved by automatically identifying the start and end points in subsequent time points through a non-rigid template-tracking algorithm. Once the start and end points have been located, the minimal cost path is constructed in every time frame. We compare the proposed approach to two alternative approaches: The first one is based on a semi-automatic extraction of the coronaries with start and end points manually supplied in each time frame and the second approach is based on propagating the extracted coronaries from the end-diastolic time frame to other time frames using non-rigid registration. Our results show that the proposed approach performs significantly better than non-rigid registration based method and that the resulting motion model is comparable to the motion model constructed from semi-automatic extractions of the coronaries.
{"title":"Coronary artery motion modeling from 3D cardiac CT sequences using template matching and graph search","authors":"D. Zhang, L. Risser, C. Metz, L. Neefjes, N. Mollet, W. Niessen, D. Rueckert","doi":"10.1109/ISBI.2010.5490171","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490171","url":null,"abstract":"In this paper we present a method for coronary artery motion tracking in 4D cardiac CT data sets. The algorithm allows the automatic construction of a 4D coronary motion model from pre-operative CT which can be used for guiding totally-endoscopic coronary artery bypass surgery (TECAB). The proposed approach is based on two steps: In the first step, the coronary arteries are extracted in the end-diastolic time frame using a minimal cost path approach. To achieve this, the start and end points of the coronaries are identified interactively and the minimal cost path between the start and end points is computed using the A* graph algorithm. In the second stage the coronaries are tracked automatically through all other phases of the cardiac cycle. This is achieved by automatically identifying the start and end points in subsequent time points through a non-rigid template-tracking algorithm. Once the start and end points have been located, the minimal cost path is constructed in every time frame. We compare the proposed approach to two alternative approaches: The first one is based on a semi-automatic extraction of the coronaries with start and end points manually supplied in each time frame and the second approach is based on propagating the extracted coronaries from the end-diastolic time frame to other time frames using non-rigid registration. Our results show that the proposed approach performs significantly better than non-rigid registration based method and that the resulting motion model is comparable to the motion model constructed from semi-automatic extractions of the coronaries.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"75 Suppl 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127307851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490229
K. Chaudhury, Zsuzsanna Püspöki, A. Muñoz-Barrutia, D. Sage, M. Unser
We propose a fast algorithm for the detection of cells in fluorescence images. The algorithm, which estimates the number of cells and their respective centers and radii, relies on the fast computation of intensity-based correlations between the cells and a near-isotropic Mexican-hat-like detector. The attractive features of our algorithm are its speed and accuracy. The former attribute is derived from the fact that we can compute correlations between a cell and detectors of various sizes using O(1) operations; whereas, it is our ability to continuously control the center and the radius of the detector that results in a precise estimate of the position and size of the cell. We provide experimental results on both simulated and real data to demonstrate the speed and accuracy of the algorithm.
{"title":"Fast detection of cells using a continuously scalable Mexican-hat-like template","authors":"K. Chaudhury, Zsuzsanna Püspöki, A. Muñoz-Barrutia, D. Sage, M. Unser","doi":"10.1109/ISBI.2010.5490229","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490229","url":null,"abstract":"We propose a fast algorithm for the detection of cells in fluorescence images. The algorithm, which estimates the number of cells and their respective centers and radii, relies on the fast computation of intensity-based correlations between the cells and a near-isotropic Mexican-hat-like detector. The attractive features of our algorithm are its speed and accuracy. The former attribute is derived from the fact that we can compute correlations between a cell and detectors of various sizes using O(1) operations; whereas, it is our ability to continuously control the center and the radius of the detector that results in a precise estimate of the position and size of the cell. We provide experimental results on both simulated and real data to demonstrate the speed and accuracy of the algorithm.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129927900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490217
Fabrice Michel, N. Paragios
The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method that models the variations of intensities through the use of linear combinations of kernels that are able to handle intensity shifts. Such transport functions are considered as the singleton potentials of a Markov Random Field (MRF) where pair-wise connections encode smoothness as well as prior knowledge through a local neighborhood system. We use recent advances in the field of discrete optimization towards recovering the lowest potential of the designed cost function. Promising results on real data demonstrate the potentials of our approach.
{"title":"Image transport regression using mixture of experts and discrete Markov Random Fields","authors":"Fabrice Michel, N. Paragios","doi":"10.1109/ISBI.2010.5490217","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490217","url":null,"abstract":"The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method that models the variations of intensities through the use of linear combinations of kernels that are able to handle intensity shifts. Such transport functions are considered as the singleton potentials of a Markov Random Field (MRF) where pair-wise connections encode smoothness as well as prior knowledge through a local neighborhood system. We use recent advances in the field of discrete optimization towards recovering the lowest potential of the designed cost function. Promising results on real data demonstrate the potentials of our approach.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490102
Dan Wang, A. Tewfik
This paper presents the first demonstration of real time 3D tracking of organ deformation based on one-sided, limited view needlescopic optical imaging and a single pre-operative MRI/CT scan. The reconstruction is based on the empirical observation that the spherical harmonic coefficients corresponding to distorted surfaces of any given organ lie in lower dimensional spaces that can be learned during training. The paper discusses the details of the selection of the limited optical views and the registration of the real time partial optical images with the single pre-operative MRI/CT scan. Finally, it demonstrates the first experimental 3D reconstruction of ex-vivo kidneys based on a single MRI scan with 1 mm resolution and real time single side optical imagery achieving spatial resolution of better than 2 mm, even on the hidden organ surface, or less than 1.85% relative error.
{"title":"Real time tracking of 3D organ surfaces using single MR image and limited optical viewing","authors":"Dan Wang, A. Tewfik","doi":"10.1109/ISBI.2010.5490102","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490102","url":null,"abstract":"This paper presents the first demonstration of real time 3D tracking of organ deformation based on one-sided, limited view needlescopic optical imaging and a single pre-operative MRI/CT scan. The reconstruction is based on the empirical observation that the spherical harmonic coefficients corresponding to distorted surfaces of any given organ lie in lower dimensional spaces that can be learned during training. The paper discusses the details of the selection of the limited optical views and the registration of the real time partial optical images with the single pre-operative MRI/CT scan. Finally, it demonstrates the first experimental 3D reconstruction of ex-vivo kidneys based on a single MRI scan with 1 mm resolution and real time single side optical imagery achieving spatial resolution of better than 2 mm, even on the hidden organ surface, or less than 1.85% relative error.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131948485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490136
T. Sørensen, K. O. Noe, Christian P. V. Christoffersen, Martin Kristiansen, K. Mouridsen, O. Østerby, L. Brix
Using variational calculus we develop an active contour model to segment an object across a number of image frames in the presence of an optical flow field. We define an energy functional that is locally minimized when the object is tracked across the entire image stack. Unlike classical snakes, image forces and regularization terms are integrated over the full set of images in the proposed model. This results in a new formulation of active contours. The method is demonstrated by segmenting the ascending aorta in a phase-contrast cine MRI dataset. Techniques to compute the required optical flow field and a “one-click” contour initialization step are suggested for this particular modality.
{"title":"Active contours in optical flow fields for image sequence segmentation","authors":"T. Sørensen, K. O. Noe, Christian P. V. Christoffersen, Martin Kristiansen, K. Mouridsen, O. Østerby, L. Brix","doi":"10.1109/ISBI.2010.5490136","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490136","url":null,"abstract":"Using variational calculus we develop an active contour model to segment an object across a number of image frames in the presence of an optical flow field. We define an energy functional that is locally minimized when the object is tracked across the entire image stack. Unlike classical snakes, image forces and regularization terms are integrated over the full set of images in the proposed model. This results in a new formulation of active contours. The method is demonstrated by segmenting the ascending aorta in a phase-contrast cine MRI dataset. Techniques to compute the required optical flow field and a “one-click” contour initialization step are suggested for this particular modality.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"3 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127980437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490155
H. Schomberg
Magnetic Particle Imaging is an emerging reconstructive imaging method that can create images of the spatial distribution of magnetizable nanoparticles in an object. A magnetic particle image is reconstructed by solving a discrete approximation to a linear integral equation that models the data acquisition. So far, an explicit formula for the kernel of this integral equation has been missing, forcing one to determine the matrix of the linear equation to be solved by time consuming measurements. Also, this matrix is huge and dense so that the reconstruction times tend to be long. Here, we present an explicit formula for the kernel of the modeling integral operator, transform this operator into a spatial convolution operator, and point out fast reconstruction algorithms that make use of Nonuniform Fast Fourier Transforms.
{"title":"Magnetic particle imaging: Model and reconstruction","authors":"H. Schomberg","doi":"10.1109/ISBI.2010.5490155","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490155","url":null,"abstract":"Magnetic Particle Imaging is an emerging reconstructive imaging method that can create images of the spatial distribution of magnetizable nanoparticles in an object. A magnetic particle image is reconstructed by solving a discrete approximation to a linear integral equation that models the data acquisition. So far, an explicit formula for the kernel of this integral equation has been missing, forcing one to determine the matrix of the linear equation to be solved by time consuming measurements. Also, this matrix is huge and dense so that the reconstruction times tend to be long. Here, we present an explicit formula for the kernel of the modeling integral operator, transform this operator into a spatial convolution operator, and point out fast reconstruction algorithms that make use of Nonuniform Fast Fourier Transforms.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131332795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490353
Gonzalo Vegas-Sánchez-Ferrero, D. Martín-Martínez, S. Aja‐Fernández, C. Palencia
The influence of the cartesian interpolation of ultrasound data over the final image statistical model is studied. When fully formed speckle is considered and no compression of the data is done, we show that the interpolated final image can be modeled following a Gamma distribution, which is a good approximation for the weighted sum of Rayleigh variables. The importance of taking into account the interpolation stage to statistically model ultrasound images is pointed out. The interpolation model here proposed can be easily extended to more complex distributions.
{"title":"On the influence of interpolation on probabilistic models for ultrasonic images","authors":"Gonzalo Vegas-Sánchez-Ferrero, D. Martín-Martínez, S. Aja‐Fernández, C. Palencia","doi":"10.1109/ISBI.2010.5490353","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490353","url":null,"abstract":"The influence of the cartesian interpolation of ultrasound data over the final image statistical model is studied. When fully formed speckle is considered and no compression of the data is done, we show that the interpolated final image can be modeled following a Gamma distribution, which is a good approximation for the weighted sum of Rayleigh variables. The importance of taking into account the interpolation stage to statistically model ultrasound images is pointed out. The interpolation model here proposed can be easily extended to more complex distributions.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125568404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-04-14DOI: 10.1109/ISBI.2010.5490391
M. Sapir, F. Khan, Yevgen Vengrenyuk, G. Fernandez, R. Mesa-Tejada, Stefan Hamman, M. Teverovskiy, M. Donovan
We present a novel improvement of our previously published image analysis system for the automated localization and quantification of protein biomarker expression in immunofluorescence (IF) microscopic images. The improvement has been developed primarily for biopsy based images which are by nature of variable quality and heterogeneous. The innovative method is employed for discriminating the biomarker signal from background, where signal may be the expression of multiple biomarkers or counterstains used in IF. The method is dynamic and it derives a threshold for a true biomarker signal based on the relationship between disease and non-disease tissue components. In addition, a new dynamic range feature construction is presented that is less affected by processing and other variations in tissue. The utility of the approach is demonstrated in predicting, based on the diagnostic biopsy tissue, prostate cancer disease progression within eight years after a radical prostatectomy. For this purpose, androgen receptor (AR) and Ki67 biomarker expression in prostate biopsy samples was quantified and features from the proposed approach were shown to be associated with disease progression in a univariate analysis and manifested improved performance over prior systems. Furthermore, AR and Ki67 features were selected in a multivariate model integrating clinical, histological, and biomarker features, proving their independent prognostic value.
{"title":"Improved automated localization and quantification of protein multiplexes via multispectral fluorescence imaging in heterogenous biopsy samples","authors":"M. Sapir, F. Khan, Yevgen Vengrenyuk, G. Fernandez, R. Mesa-Tejada, Stefan Hamman, M. Teverovskiy, M. Donovan","doi":"10.1109/ISBI.2010.5490391","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490391","url":null,"abstract":"We present a novel improvement of our previously published image analysis system for the automated localization and quantification of protein biomarker expression in immunofluorescence (IF) microscopic images. The improvement has been developed primarily for biopsy based images which are by nature of variable quality and heterogeneous. The innovative method is employed for discriminating the biomarker signal from background, where signal may be the expression of multiple biomarkers or counterstains used in IF. The method is dynamic and it derives a threshold for a true biomarker signal based on the relationship between disease and non-disease tissue components. In addition, a new dynamic range feature construction is presented that is less affected by processing and other variations in tissue. The utility of the approach is demonstrated in predicting, based on the diagnostic biopsy tissue, prostate cancer disease progression within eight years after a radical prostatectomy. For this purpose, androgen receptor (AR) and Ki67 biomarker expression in prostate biopsy samples was quantified and features from the proposed approach were shown to be associated with disease progression in a univariate analysis and manifested improved performance over prior systems. Furthermore, AR and Ki67 features were selected in a multivariate model integrating clinical, histological, and biomarker features, proving their independent prognostic value.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126493302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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