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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 four livers (A to D).(A1) to (D1) show the meshes of the liver; (A2) to (D2) show the wired grid of the liver; (A3) to (D3) show the ground truth of the liver surface; (A4) to (D4) show the color map of the segmentation error on the surface of the liver.
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pone.0118064.g011: Segmentation results of four livers (A to D).(A1) to (D1) show the meshes of the liver; (A2) to (D2) show the wired grid of the liver; (A3) to (D3) show the ground truth of the liver surface; (A4) to (D4) show the color map of the segmentation error on the surface of the liver.

Mentions: Fig. 11 shows four examples of the segmentation error distribution on the surface of the liver. The first to the fourth rows show the four different liver data sets. The first column shows the segmented mesh of the liver, and the second column shows the wired grid of the segmented mesh. The third column shows the ground truth segmentation results of the corresponding liver data, and the fourth column provides the color map of the segmentation error distribution on the surface of the liver. The fifth column provides the color bar and its corresponding density distributions. From the four groups of data sets, the segmentation results are steadily constant at the flat region of the liver, and most of the errors are distributed at the sharp corners or concave regions. In addition, the mean segmentation error is 1.6 mm, and the errors for 83.4% of the mesh vertices are less than 2.0 mm. evidently, the proposed method is significantly effective and robust for the segmentation of liver from CT images.


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 four livers (A to D).(A1) to (D1) show the meshes of the liver; (A2) to (D2) show the wired grid of the liver; (A3) to (D3) show the ground truth of the liver surface; (A4) to (D4) show the color map of the segmentation error on the surface of the liver.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0118064.g011: Segmentation results of four livers (A to D).(A1) to (D1) show the meshes of the liver; (A2) to (D2) show the wired grid of the liver; (A3) to (D3) show the ground truth of the liver surface; (A4) to (D4) show the color map of the segmentation error on the surface of the liver.
Mentions: Fig. 11 shows four examples of the segmentation error distribution on the surface of the liver. The first to the fourth rows show the four different liver data sets. The first column shows the segmented mesh of the liver, and the second column shows the wired grid of the segmented mesh. The third column shows the ground truth segmentation results of the corresponding liver data, and the fourth column provides the color map of the segmentation error distribution on the surface of the liver. The fifth column provides the color bar and its corresponding density distributions. From the four groups of data sets, the segmentation results are steadily constant at the flat region of the liver, and most of the errors are distributed at the sharp corners or concave regions. In addition, the mean segmentation error is 1.6 mm, and the errors for 83.4% of the mesh vertices are less than 2.0 mm. evidently, the proposed method is significantly effective and robust for the segmentation of liver from CT images.

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