A Generic Probabilistic Active Shape Model for Organ Segmentation

Wimmer, Andreas; Soza, Grzegorz; Hornegger, Joachim

doi:10.1007/978-3-642-04271-3_4

Cited by 60 publications

(46 citation statements)

References 13 publications

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“…Compared to state-of-the-art semiautomatic and interactive methods, 6,9,16 this interactive requirement is quite low. Compared to prior model based methods, 3,12,26 the model is not restricted by training data and can be applied to livers with any shape. In comparison with methods sequentially segmenting 2D slices, the proposed 3D segmentation model utilizes information from 3D neighborhood and does not have the problem of error accumulation.…”

Section: Discussionmentioning

confidence: 99%

“…We can see that the proposed model performs better than RAP in terms of distance error. Table V shows the comparative results with state-ofthe-art methods including Maklad et al, 16 Beichel et al, 9 Afifi and Nakaguchi, 7 RAP et al, 5 Peng et al, 6 Kainmuller et al, 3 and Wimmer et al 26 Besides total scores and running times, detailed errors with means and variances for all the five measures are also listed. The box-plots in Fig.…”

Section: E Perceptual Comparison With the Rap Modelmentioning

confidence: 99%

“…The user interaction per case requires 16 min on average. Five methods including Maklad, 16 Peng, 6 RAP, 5 Wimmer, 26 and Afifi 7 are semiautomatic with different interaction requirements. Maklad 16 achieved the best score on the 10 MICCAI-Testing data.…”

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confidence: 99%

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3D liver segmentation using multiple region appearances and graph cuts

Peng

et al. 2015

Medical Physics

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Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4 ± 3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7% ± 0.5% and 97.5% ± 0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. Conclusions: A multiregion-appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state-of-the-art methods. Validation results suggest that it may be suitable for the clinical use. C 2015 American Association of Physicists in Medicine. [http://dx

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Section: Discussionmentioning

confidence: 99%

Section: E Perceptual Comparison With the Rap Modelmentioning

confidence: 99%

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3D liver segmentation using multiple region appearances and graph cuts

Peng

et al. 2015

Medical Physics

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“…Such active shape models are widely used to characterize structures in medical images [5] as well as for biomedical image segmentation [6]. There exist also variants of the technique that make use of non-linear algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Closed-form alignment of active surface models using splines

Schmitter

Unser

2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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We propose a new formulation of the active surface model in 3D. Instead of aligning a shape dictionary through the similarity transform, we consider more flexible affine transformations and introduce an alignment method that is unbiased in the sense that it implicitly constructs a common reference shape. Our formulation is expressed in the continuous domain and we provide an algorithm to exactly implement the framework using spline-based parametric surfaces. We test our model on real 3D MRI data. A comparison with the classical active shape model shows that our method allows us to capture shape variability in a dictionary in a more precise way.Index Terms-Active surface model, 3D, parametric surfaces, splines, continuous domain Fig. 1. Real data set consisting of 14 3D MRI scans. The red meshes outline the segmented descending thoracic aorta.

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“…In fact, Heimann and Meinzer observe in their recent review [5] that nonlinear, landmark-based SSMs have hardly attracted the attention of the community so far. This stands in contrast to level set segmentation with shape priors, where nonlinear techniques such as KPCA [6] or Parzen Density estimation [7] are becoming increasingly popular for learning priors from signed distance functions.…”

Section: Introductionmentioning

confidence: 99%

3D Active Shape Model Segmentation with Nonlinear Shape Priors

Kirschner

Becker

Wesarg

2011

Lecture Notes in Computer Science

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Abstract. The Active Shape Model (ASM) is a segmentation algorithm which uses a Statistical Shape Model (SSM) to constrain segmentations to 'plausible' shapes. This makes it possible to robustly segment organs with low contrast to adjacent structures. The standard SSM assumes that shapes are Gaussian distributed, which implies that unseen shapes can be expressed by linear combinations of the training shapes. Although this assumption does not always hold true, and several nonlinear SSMs have been proposed in the literature, virtually all applications in medical imaging use the linear SSM. In this work, we investigate 3D ASM segmentation with a nonlinear SSM based on Kernel PCA. We show that a recently published energy minimization approach for constraining shapes with a linear shape model extends to the nonlinear case, and overcomes shortcomings of previously published approaches. Our approach for nonlinear ASM segmentation is applied to vertebra segmentation and evaluated against the linear model.

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A Generic Probabilistic Active Shape Model for Organ Segmentation

Cited by 60 publications

References 13 publications

3D liver segmentation using multiple region appearances and graph cuts

3D liver segmentation using multiple region appearances and graph cuts

Closed-form alignment of active surface models using splines

3D Active Shape Model Segmentation with Nonlinear Shape Priors

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