3D Anatomical Shape Atlas Construction Using Mesh Quality Preserved Deformable Models

Cui, Xinyi; Zhang, Shaoting; Zhan, Yiqiang; Gao, Mingchen; Huang, Junzhou; Metaxas, Dimitris N.

doi:10.1007/978-3-642-33463-4_2

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Anatomical atlases are becoming widespread in the anatomical studies, whether it is to establish a diagnosis, or follow the evolution of a disease [24] but also in paleontology to discriminate taxonomic groups (e.g., species, genera). Atlas based on morphometric tools, that integrates the notion of variability, is more and more used [9,14].…”

Section: Framework To Estimate An Average Model 21 Atlas and Averagmentioning

confidence: 99%

How to Build an Average Model When Samples are Variably Incomplete? Application to Fossil Data

Dumoncel

Subsol

Durrleman

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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In paleontology, incomplete samples with small or large missing parts are frequently encountered. For example, dental crowns, which are widely studied in paleontology because of their potential interest in taxonomic and phylogenetic analyses, are nearly systematically affected by a variable degree of wear that alters considerably their shape. It is then difficult to compute a significant reference surface model based on classical methods which are used to build atlases from set of samples. In this paper, we present a general approach to deal with the problem of estimating an average model from a set of incomplete samples. Our method is based on a state-of-the-art non-rigid surface registration algorithm. In a first step, we detect missing parts which allows one to focus only on the common parts to get an accurate registration result. In a second step, we try to build average model of the missing parts by using information which is available in a subset of the samples. We specifically apply our method on teeth, and more precisely on the surface in between dentine and enamel tissues (EDJ). We investigate the robustness and accuracy properties of the methods on a set of artificial samples representing a high degree of incompleteness. We compare the reconstructed complete shape to a ground-truth dataset. We then show some results on real data.

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Section: Framework To Estimate An Average Model 21 Atlas and Averagmentioning

confidence: 99%

How to Build an Average Model When Samples are Variably Incomplete? Application to Fossil Data

Dumoncel

Subsol

Durrleman

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…The one-to-one correspondence of vertices on different meshes is obtained by taking an arbitrary shape in the repository as the reference and registering it to the others using adaptive focus deformable model (AFDM). 32,33 To remove the bias caused by the selection of the reference, the mean shape is computed and then is registered to all the shapes again. For a patient who needs a surgery plan, a rough initial liver segmentation based on simple region growing method is rapidly performed.…”

Section: Iiia Sparse Shape Representation For Livermentioning

confidence: 99%

A new segmentation framework based on sparse shape composition in liver surgery planning system

Wang

Zhang

et al. 2013

Medical Physics

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Purpose: To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system. Methods: The segmentation framework proposed in this paper includes two important modules:(1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer. Results: Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively. Conclusions: The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments.

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