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Adaptive Mesh Expansion Model (AMEM) for liver segmentation from CT image.

Wang X, Yang J, Ai D, Zheng Y, Tang S, Wang Y - PLoS ONE (2015)

Bottom Line: The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver.Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms.Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.

View Article: PubMed Central - PubMed

Affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.

ABSTRACT
This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily manipulated. The balloon, edge, and gradient forces are combined with the binary image to construct the external force of the deformable model, which can rapidly drive the DSM to approach the target liver boundaries. Moreover, tangential and normal forces are combined with the gradient image to control the internal force, such that the DSM degree of smoothness can be precisely controlled. The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver. The proposed method is evaluated on the basis of different criteria applied to 10 clinical data sets. Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms. Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.

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Segmentation results of the proposed method for three different CT data sets.(A), (B) and (C) demonstrate results of three different data sets.
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pone.0118064.g010: Segmentation results of the proposed method for three different CT data sets.(A), (B) and (C) demonstrate results of three different data sets.

Mentions: Fig. 9 demonstrates the final segmentation results of the proposed AMEM-based method. In this fig., S30 to S190 represent the slice number of the 3D volume data in the transverse direction. The proposed AMEM-based method is significantly effective for the whole volume data of the liver. Although the intensity distribution of the liver in several regions is closer to the other tissues, the AMEM-based method is still capable of segmenting the liver from the background tissues. Fig. 10 shows the final segmentation results of the proposed method from three CT data sets. In this figure, the left three columns show the segmentation results in the transverse, sagittal, and coronal directions, and the fourth column shows the magnified views corresponding to the marked region in the left three columns. From the experiments, the sharp corner areas of the liver are precisely segmented by the proposed AMEM-based method.


Adaptive Mesh Expansion Model (AMEM) for liver segmentation from CT image.

Wang X, Yang J, Ai D, Zheng Y, Tang S, Wang Y - PLoS ONE (2015)

Segmentation results of the proposed method for three different CT data sets.(A), (B) and (C) demonstrate results of three different data sets.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4358832&req=5

pone.0118064.g010: Segmentation results of the proposed method for three different CT data sets.(A), (B) and (C) demonstrate results of three different data sets.
Mentions: Fig. 9 demonstrates the final segmentation results of the proposed AMEM-based method. In this fig., S30 to S190 represent the slice number of the 3D volume data in the transverse direction. The proposed AMEM-based method is significantly effective for the whole volume data of the liver. Although the intensity distribution of the liver in several regions is closer to the other tissues, the AMEM-based method is still capable of segmenting the liver from the background tissues. Fig. 10 shows the final segmentation results of the proposed method from three CT data sets. In this figure, the left three columns show the segmentation results in the transverse, sagittal, and coronal directions, and the fourth column shows the magnified views corresponding to the marked region in the left three columns. From the experiments, the sharp corner areas of the liver are precisely segmented by the proposed AMEM-based method.

Bottom Line: The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver.Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms.Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.

View Article: PubMed Central - PubMed

Affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.

ABSTRACT
This study proposes a novel adaptive mesh expansion model (AMEM) for liver segmentation from computed tomography images. The virtual deformable simplex model (DSM) is introduced to represent the mesh, in which the motion of each vertex can be easily manipulated. The balloon, edge, and gradient forces are combined with the binary image to construct the external force of the deformable model, which can rapidly drive the DSM to approach the target liver boundaries. Moreover, tangential and normal forces are combined with the gradient image to control the internal force, such that the DSM degree of smoothness can be precisely controlled. The triangular facet of the DSM is adaptively decomposed into smaller triangular components, which can significantly improve the segmentation accuracy of the irregularly sharp corners of the liver. The proposed method is evaluated on the basis of different criteria applied to 10 clinical data sets. Experiments demonstrate that the proposed AMEM algorithm is effective and robust and thus outperforms six other up-to-date algorithms. Moreover, AMEM can achieve a mean overlap error of 6.8% and a mean volume difference of 2.7%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 1.3 mm and 2.7 mm, respectively.

Show MeSH