The Experts below are selected from a list of 4176 Experts worldwide ranked by ideXlab platform
Shigeru Nawano - One of the best experts on this subject based on the ideXlab platform.
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automated liver segmentation from a postmortem ct scan based on a Statistical Shape Model
International Journal of Computer Assisted Radiology and Surgery, 2017Co-Authors: Atsushi Saito, Seiji Yamamoto, Shigeru Nawano, Akinobu ShimizuAbstract:Purpose Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a Statistical Shape Model (SSM) for a postmortem liver.
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Automated liver segmentation from a postmortem CT scan based on a Statistical Shape Model
International Journal of Computer Assisted Radiology and Surgery, 2017Co-Authors: Atsushi Saito, Seiji Yamamoto, Shigeru Nawano, Akinobu ShimizuAbstract:Purpose Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a Statistical Shape Model (SSM) for a postmortem liver. Methods The location and Shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation–maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and Shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label. Results The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with Statistically significant difference. Conclusions We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and Shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.
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Statistical Shape Model of a liver for autopsy imaging
Computer Assisted Radiology and Surgery, 2014Co-Authors: Atsushi Saito, Akinobu Shimizu, Hidefumi Watanabe, Seiji Yamamoto, Shigeru Nawano, Hidefumi KobatakeAbstract:Purpose Modeling the postmortem liver for autopsy imaging is a challenging problem owing to the variation in organ deformation found in cadavers and limited availability of postmortem liver CT scans. An algorithm was developed to construct a Statistical Shape Model (SSM) for the adult postmortem liver in autopsy imaging.
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a conditional Statistical Shape Model with integrated error estimation of the conditions application to liver segmentation in non contrast ct images
Medical Image Analysis, 2014Co-Authors: Sho Tomoshige, Akinobu Shimizu, Hidefumi Watanabe, Elco Oost, Shigeru NawanoAbstract:Abstract This paper presents a novel conditional Statistical Shape Model in which the condition can be relaxed instead of being treated as a hard constraint. The major contribution of this paper is the integration of an error Model that estimates the reliability of the observed conditional features and subsequently relaxes the conditional Statistical Shape Model accordingly. A three-step pipeline consisting of (1) conditional feature extraction from a maximum a posteriori estimation, (2) Shape prior estimation through the novel level set based conditional Statistical Shape Model with integrated error Model and (3) subsequent graph cuts segmentation based on the estimated Shape prior is applied to automatic liver segmentation from non-contrast abdominal CT volumes. Comparison with three other state of the art methods shows the superior performance of the proposed algorithm.
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Relaxed Conditional Hierarchical Statistical Shape Model of Multiple Organs
2013 First International Symposium on Computing and Networking, 2013Co-Authors: Reito Oshima, Atsushi Saito, Akinobu Shimizu, Hidefumi Watanabe, Shigeru NawanoAbstract:This paper proposes a relaxed conditional hierarchical Statistical Shape Model (SSM) of multiple organs. After extracting Shape and pose parameters from the training label dataset of multiple organs, the Shape Model and the pose Model of each organ are constructed by principal component analysis (PCA). Subsequently, the principal scores of all organs are concatenated into a vector, and the vectors computed from the training dataset are forwarded to the PCA-based Statistical Modeling of the multiple organs under conditions of their neighboring organs. A relaxation scheme is introduced, to take into account errors in the conditions. This study focuses on Modeling of a spleen and a gallbladder given a liver as a conditional organ. The performance of the Model was evaluated with the measures of generalization and specificity, which were computed by three-fold cross-validation using labels of 27 abdominal CT volumes with the size of 170 × 170 × 110 voxels and a resolution of 1.8809 mm/voxel. Compared with a hierarchical SSM without conditions, generalization and specificity were improved from 0.488 to 0.506 and from 0.319 to 0.328 on average, respectively. In addition, the proposed relaxed conditional hierarchical SSM outperformed a hierarchical SSM with hard conditions. The performance indices were improved by 0.040 and 0.010 for generalization and specificity, respectively.
Yen-wei Chen - One of the best experts on this subject based on the ideXlab platform.
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quantitative imaging quantification of liver Shape on ct using the Statistical Shape Model to evaluate hepatic fibrosis
Academic Radiology, 2015Co-Authors: Masatoshi Hori, Yen-wei Chen, Toshiyuki Okada, Yoshinobu Sato, Keisuke Higashiura, Hiromitsu Onishi, Hidetoshi Eguchi, Hiroaki NaganoAbstract:Rationale and Objectives To investigate the usefulness of the Statistical Shape Model (SSM) for the quantification of liver Shape to evaluate hepatic fibrosis. Materials and Methods Ninety-one subjects (45 men and 46 women; age range, 20–75 years) were included in this retrospective study: 54 potential liver donors and 37 patients with chronic liver disease. The subjects were classified histopathologically according to the fibrosis stage as follows: F0 ( n = 55); F1 ( n = 6); F2 (3); F3 ( n = 1); and F4 ( n = 26). Each subject underwent contrast-enhanced computed tomography (CT) using a 64-channel scanner (0.625-mm slice thickness). An abdominal radiologist manually traced the liver boundaries on every CT section using an image workstation; the boundaries were used for subsequent analyses. An SSM was constructed by the principal component analysis of the subject data set, which defined a parametric Model of the liver Shapes. The Shape parameters were calculated by fitting SSM to the segmented liver Shape of each subject and were used for the training of a linear support vector regression (SVR), which classifies the liver fibrosis stage to maximize the area under the receiver operating characteristic curve (AUC). SSM/SVR Models were constructed and were validated in a leave-one-out manner. The performance of our technique was compared to those of two previously reported types of caudate–right lobe ratios (C/RL-m and C/RL-r). Results In our SSM/SVR Models, the AUC values for the classification of liver fibrosis were 0.96 (F0 vs. F1–4), 0.95 (F0–1 vs. F2–4), 0.96 (F0–2 vs. F3–4), and 0.95 (F0–3 vs. F4). These values were significantly superior to AUC values using the C/RL-m or C/RL-r ratios ( P Conclusions SSM was useful for estimating the stage of hepatic fibrosis by quantifying liver Shape.
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Statistical Shape Model of the Liver and Its Application to Computer‐Aided Diagnosis of Liver Cirrhosis
Electrical Engineering in Japan, 2014Co-Authors: Mei Uetani, Shinya Kohara, Tomoko Tateyama, Akira Furukawa, Shuzo Kanasaki, Hidetoshi Tanaka, Yen-wei ChenAbstract:SUMMARY In recent years, there has been increasing interest in Statistical Shape Modeling of human anatomy. The Statistical Shape Model can capture the morphological variations of human anatomy. Since liver cirrhosis will cause significant morphological changes, the authors propose a computer-aided diagnosis method for liver cirrhosis based on Statistical Shape Models. In the proposed method, the authors first construct a Statistical Shape Model of the liver using 50 clinical CT datasets (25 sets of normal data and 25 sets of abnormal data). The authors apply the marching cubes algorithm to convert the segmented liver volume to a triangulated mesh surface containing 1000 vertex points. The coordinates of these vertex points are used to represent the 3D liver Shape as a Shape vector. After normalization and identification of correspondences between all datasets, principal component analysis (PCA) is employed to find the principal variation modes of the Shape vectors. Then the authors propose a mode selection method based on class variations between the normal class and abnormal class. The authors found that the top two modes of class variations are most effective for the classification of normal and abnormal livers. The classification rate of abnormal livers and normal livers by the use of a simple linear discriminant function were 84% and 80%, respectively.
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Statistical Shape Model of the liver and effective mode selection for classification of liver cirrhosis
2012 6th International Conference on New Trends in Information Science Service Science and Data Mining (ISSDM2012), 2012Co-Authors: Yen-wei Chen, Tomoko Tateyama, Akira Furukawa, Shuzo Kanasaki, Huiyan JiangAbstract:In computational anatomy, Statistical Shape Model is used for quantitative evaluation of the variations of an organ Shape. Since liver cirrhosis will cause significant hepatic morphological changes, we applied Statistical Shape Model of the liver to capture the morphological changes and recognize whether a liver is normal or abnormal. In this paper, we propose an effective mode selection method to improve the classification accuracy. In addition to the conventional Accumulated Variance Contribution Rate (AVCR) based mode selection, we newly propose a Pearson correlation based mode selection method and combine them to select the effective modes. The coefficients of the selected modes (components) are used as features to recognize whether liver is normal or abnormal. The effectiveness of the proposed method is evaluated by the classification accuracy of normal and abnormal. Experimental results show that our proposed method is superior than conventional methods.
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Preliminary study on Statistical Shape Model applied to diagnosis of liver cirrhosis
2011 18th IEEE International Conference on Image Processing, 2011Co-Authors: Shinya Kohara, Tomoko Tateyama, Amir Hossein Foruzan, Akira Furukawa, Shuzo Kanasaki, Makoto Wakamiya, Yen-wei ChenAbstract:In computational anatomy, Statistical Shape Model (SSM) is used for the quantitative evaluation of variations in the Shapes of different organs. This paper focuses on the construction of a SSM of the liver and its application to computer-assisted diagnosis of cirrhosis. We prove the potential application of SSMs in the classification of normal and cirrhotic livers. In constructing a SSM of the liver, we first normalize volume data followed by the construction of the Model using principal component analysis. The coefficients of the Model are used as indicators of liver pathology. The effectiveness of the constructed Model is evaluated by the classification accuracy of both normal and abnormal data.
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Application of Statistical Shape Model to diagnosis of liver disease
The 2nd International Conference on Software Engineering and Data Mining, 2010Co-Authors: Shinya Kohara, Tomoko Tateyama, Amir Hossein Foruzan, Akira Furukawa, Shuzo Kanasaki, Makoto Wakamiya, Yen-wei ChenAbstract:In computational anatomy, Statistical Shape Model is used for quantitative evaluation of the variations of an organ Shape. This paper is focused on construction of Statistical Shape Model of the liver and its application to computer assisted diagnosis. We prove the potential application of Statistical Shape Models in classification of normal and cirrhosis livers. First, Statistical Shape Model of liver is constructed. Then the coefficients of the Model are used to recognize whether liver is normal or abnormal.
Akinobu Shimizu - One of the best experts on this subject based on the ideXlab platform.
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IVCNZ - Evaluation of a Statistical Shape Model for the Liver
2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2018Co-Authors: Zhihui Lu, Akinobu Shimizu, Harvey HoAbstract:A Statistical Shape Model (SSM)for the liver has been proposed recently which is based on a cubic Hermite mesh with a small number of elements. By fitting to the data cloud of a liver surface, such a mesh is capable of capturing the complex liver Shape with fine details. However, the liver SSM suffers from uncertain nodal correspondence in particular at locations where local salient Shape features lead to incompatible/incorrect node correspondence. Furthermore, the fitting error is evaluated by the sum of all projection vectors without differentiating the fitting quality at local regions. In this work we assess the quality of node correspondence by using generalisation and specificity measures. Moreover we use a Jaccard index (JI)to evaluate the overall quality of fitting error. We found that 4-element mesh yielded the least fitting error, and the mesh also had a high generalisation.
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Evaluation of a Statistical Shape Model for the Liver
2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), 2018Co-Authors: Zhihui Lu, Akinobu Shimizu, Harvey HoAbstract:A Statistical Shape Model (SSM)for the liver has been proposed recently which is based on a cubic Hermite mesh with a small number of elements. By fitting to the data cloud of a liver surface, such a mesh is capable of capturing the complex liver Shape with fine details. However, the liver SSM suffers from uncertain nodal correspondence in particular at locations where local salient Shape features lead to incompatible/incorrect node correspondence. Furthermore, the fitting error is evaluated by the sum of all projection vectors without differentiating the fitting quality at local regions. In this work we assess the quality of node correspondence by using generalisation and specificity measures. Moreover we use a Jaccard index (JI)to evaluate the overall quality of fitting error. We found that 4-element mesh yielded the least fitting error, and the mesh also had a high generalisation.
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Disorder Development Onset Prediction Based on Spatiotemporal Statistical Shape Model
2018 IEEE International Conference on Systems Man and Cybernetics (SMC), 2018Co-Authors: Saadia Binte Alam, Akinobu Shimizu, Kumiko Ando, Reiichi Ishikura, Syoji KobashiAbstract:During the early developmental stage, the brain undergoes more changes in size, Shape, and appearance than at any other stage in life. A better understanding of brain development can decrease the symptom of development disorder through very early detection and application of remedial education. In this paper, we present a computer-aided diagnosis (CAD) system, which estimates onset probability of brain development disorder using neonatal brain MR images. The CAD system first constructs spatiotemporal Statistical Shape Model (stSSM) of neonatal brain, extracts static and dynamic morphological features, and estimates the probability using machine learning techniques. This paper proposes the stSSM construction method which produces temporally continuous Eigenvectors by extending previous EM-based-stSSM construction method. The method has been validated by applying it to 12 neonatal brains whose revised ages are between - 5 to 730 days.
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automated liver segmentation from a postmortem ct scan based on a Statistical Shape Model
International Journal of Computer Assisted Radiology and Surgery, 2017Co-Authors: Atsushi Saito, Seiji Yamamoto, Shigeru Nawano, Akinobu ShimizuAbstract:Purpose Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a Statistical Shape Model (SSM) for a postmortem liver.
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Automated liver segmentation from a postmortem CT scan based on a Statistical Shape Model
International Journal of Computer Assisted Radiology and Surgery, 2017Co-Authors: Atsushi Saito, Seiji Yamamoto, Shigeru Nawano, Akinobu ShimizuAbstract:Purpose Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a Statistical Shape Model (SSM) for a postmortem liver. Methods The location and Shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation–maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and Shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label. Results The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with Statistically significant difference. Conclusions We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and Shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.
Xiuli Li - One of the best experts on this subject based on the ideXlab platform.
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Automatic liver segmentation from CT scans based on a Statistical Shape Model
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Xing Zhang, Jie Tian, Kexin Deng, Yongfang Wu, Xiuli LiAbstract:In this paper, we present an algorithm for automatic liver segmentation from CT scans which is based on a Statistical Shape Model. The proposed method is a hybrid method that combines three steps: 1) Localization of the average liver Shape Model in a test CT volume via 3D generalized Hough transform; 2) Subspace initialization of the Statistical Shape Model; 3) Deformation of the Shape Model to adapt to liver contour through an optimal surface detection approach based on graph theory. The proposed method is evaluated on MICCAI 2007 liver segmentation challenge datasets. The experiment results demonstrate availability of the proposed method.
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Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Xing Zhang, Kexin Deng, Yongfang Wu, Jie Tian$^*$, Xiuli LiAbstract:In this letter, we present an approach for automatic liver segmentation from computed tomography (CT) scans that is based on a Statistical Shape Model (SSM) integrated with an optimal-surface-detection strategy. The proposed method is a hybrid method that combines three steps. First, we use localization of the average liver Shape Model in a test CT volume via 3-D generalized Hough transform. Second, we use subspace initialization of the SSM through intensity and gradient profile. Third, we deform the Shape Model to adapt to liver contour through an optimal-surface-detection approach based on graph theory. The proposed method is evaluated on MICCAI 2007 liver-segmentation challenge datasets. The experiment results demonstrate availability of the proposed method.
Heinz Handels - One of the best experts on this subject based on the ideXlab platform.
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a 4d Statistical Shape Model for automated segmentation of lungs with large tumors
Medical Image Computing and Computer-Assisted Intervention, 2012Co-Authors: Matthias Wilms, Jan Ehrhardt, Heinz HandelsAbstract:Segmentation of lungs with large tumors is a challenging and time-consuming task, especially for 4D CT data sets used in radiation therapy. Existing lung segmentation methods are ineffective in these cases, because they are either not able to deal with large tumors and/or process every 3D image independently neglecting temporal information. In this paper, we present a approach for Model-based 4D segmentation of lungs with large tumors in 4D CT data sets. In our approach, a 4D Statistical Shape Model that accounts for inter- and intra-patient variability is fitted to the 4D image sequence, and the segmentation result is refined by a 4D graph-based optimal surface finding. The approach is evaluated using 10 4D CT data sets of lung tumor patients. The segmentation results are compared with a standard intensity-based approach and a 3D version of the presented Model-based segmentation method. The intensity-based approach shows a better performance for normal lungs, however, fails in presence of large lung tumors. Although overall performance of 3D and 4D Model-based segmentation is similar, the results indicate improved temporal coherence and improved robustness with respect to the segmentation parameters for the 4D Model-based segmentation.
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coupled level set segmentation using a point based Statistical Shape Model relying on correspondence probabilities
Progress in biomedical optics and imaging, 2010Co-Authors: Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Alexander Schmidtrichberg, Heinz HandelsAbstract:In this article, we propose a unified Statistical framework for image segmentation with Shape prior information. The approach combines an explicitely parameterized point-based probabilistic Statistical Shape Model (SSM) with a segmentation contour which is implicitly represented by the zero level set of a higher dimensional surface. These two aspects are unified in a Maximum a Posteriori (MAP) estimation where the level set is evolved to converge towards the boundary of the organ to be segmented based on the image information while taking into account the prior given by the SSM information. The optimization of the energy functional obtained by the MAP formulation leads to an alternate update of the level set and an update of the fitting of the SSM. We then adapt the probabilistic SSM for multi-Shape Modeling and extend the approach to multiple-structure segmentation by introducing a level set function for each structure. During segmentation, the evolution of the different level set functions is coupled by the multi-Shape SSM. First experimental evaluations indicate that our method is well suited for the segmentation of topologically complex, non spheric and multiple-structure Shapes. We demonstrate the effectiveness of the method by experiments on kidney segmentation as well as on hip joint segmentation in CT images.
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computation of a probabilistic Statistical Shape Model in a maximum a posteriori framework
Methods of Information in Medicine, 2009Co-Authors: Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Nicholas Ayache, Heinz HandelsAbstract:Objectives: When analyzing Shapes and Shape variabilities, the first step is bringing those Shapes into correspondence. This is a fundamental problem even when solved by manually determining exact correspondences such as landmarks. We developed a method to represent a mean Shape and a variability Model for a training data set based on probabilistic correspondence computed between the observations. Methods: First, the observations are matched on each other with an affine transformation found by the Expectation-Maximization Iterative-Closest-Points (EM-ICP) registration. We then propose a maximum-a-posteriori (MAP) framework in order to compute the Statistical Shape Model (SSM) parameters which result in an optimal adaptation of the Model to the observations. The optimization of the MAP explanation is realized with respect to the observation parameters and the generative Model parameters in a global criterion and leads to very efficient and closed-form solutions for (almost) all parameters. Results: We compared our probabilistic SSM to a SSM based on one-to-one correspondences and the PCA (classical SSM). Experiments on synthetic data served to test the performances on non-convex Shapes (15 training Shapes) which have proved difficult in terms of proper correspondence determination. We then computed the SSMs for real putamen data (21 training Shapes). The evaluation was done by measuring the generalization ability as well as the specificity of both SSMs and showed that especially Shape detail differences are better Modeled by the probabilistic SSM (Hausdorff distance in generalization ability ?? 25% smaller). Conclusions: The experimental outcome shows the efficiency and advantages of the new approach as the probabilistic SSM performs better in Modeling Shape details and differences.
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Generation of a Statistical Shape Model with probabilistic point correspondences and the expectation maximization- iterative closest point algorithm
International Journal of Computer Assisted Radiology and Surgery, 2008Co-Authors: Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Nicholas Ayache, Heinz HandelsAbstract:Objective: Identification of point correspondences between Shapes is required for Statistical analysis of organ Shapes differences. Since manual identification of landmarks is not a feasible option in 3D, several methods were developed to automatically find one-to-one correspondences on Shape surfaces. For unstructured point sets, however, one-to-one correspondences do not exist but correspondence probabilities can be determined. Materials and methods: A method was developed to compute a Statistical Shape Model based on Shapes which are represented by unstructured point sets with arbitrary point numbers. A fundamental problem when computing Statistical Shape Models is the determination of correspondences between the points of the Shape observations of the training data set. In the absence of landmarks, exact correspondences can only be determined between continuous surfaces, not between unstructured point sets. To overcome this problem, we introduce correspondence probabilities instead of exact correspondences. The correspondence probabilities are found by aligning the observation Shapes with the affine expectation maximization-iterative closest points (EM-ICP) registration algorithm. In a second step, the correspondence probabilities are used as input to compute a mean Shape (represented once again by an unstructured point set). Both steps are unified in a single optimization criterion which depe nds on the two parameters ‘registration transformation’ and ‘mean Shape’. In a last step, a variability Model which best represents the variability in the training data set is computed. Experiments on synthetic data sets and in vivo brain structure data sets (MRI) are then designed to evaluate the performance of our algorithm. Results: The new method was applied to brain MRI data sets, and the estimated point correspondences were compared to a Statistical Shape Model built on exact correspondences. Based on established measures of “generalization ability” and “specificity”, the estimates were very satisfactory. Conclusion: The novel algorithm for building a generative Statistical Shape Model (gSSM) does not need one-to-one point correspondences but relies solely on point correspondence probabilities for the computation of mean Shape and eigenmodes. It is well-suited for Shape analysis on unstructured point sets.
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Shape analysis using a point based Statistical Shape Model built on correspondence probabilities
Medical Image Computing and Computer-Assisted Intervention, 2007Co-Authors: Heike Hufnagel, Xavier Pennec, Jan Ehrhardt, Heinz Handels, Nicholas AyacheAbstract:A fundamental problem when computing Statistical Shape Models is the determination of correspondences between the instances of the associated data set. Often, homologies between points that represent the surfaces are assumed which might lead to imprecise mean Shape and variability results. We propose an approach where exact correspondences are replaced by evolving correspondence probabilities. These are the basis for a novel algorithm that computes a generative Statistical Shape Model. We developed an unified MAP framework to compute the Model parameters ('mean Shape' and 'modes of variation') and the nuisance parameters which leads to an optimal adaption of the Model to the set of observations. The registration of the Model on the instances is solved using the Expectation Maximization - Iterative Closest Point algorithm which is based on probabilistic correspondences and proved to be robust and fast. The alternated optimization of the MAP explanation with respect to the observation and the generative Model parameters leads to very efficient and closed-form solutions for (almost) all parameters. Experimental results on brain structure data sets demonstrate the efficiency and well-posedness of the approach. The algorithm is then extended to an automatic classification method using the k-means clustering and applied to synthetic data as well as brain structure classification problems.
