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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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Comparison of the segmentation accuracies for different methods.(A) the conventional DSM; (B) DSM constraint by gradient image; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition.
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pone.0118064.g008: Comparison of the segmentation accuracies for different methods.(A) the conventional DSM; (B) DSM constraint by gradient image; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition.

Mentions: To validate the performance of the main procedures on the final segmentation results of the proposed AMEM, the segmentation results over each iteration step for the combined effects of the key individual processing techniques are compared and evaluated. In addition, the comparison models are designed as follows: (A) the conventional DSM; (B) DSM constraint by gradient force; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; and (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition. The segmentation error is quantified as the spacing average distance error (SADE) of all the vertices between the deformable model and the true value. Fig. 8 compares the average space errors at every iteration step for the above-mentioned models. The left figure shows the calculation errors at all the iteration steps, and the right figure shows the magnified view of the last 10 iterations. The segmentation errors gradually decreases with the iteration applied from the first to the eighteenth iterations in all six methods. Furthermore, the SADE values for (D) and (E) rapidly decrease from 19.3 mm to 2.49 mm, and 2.79 mm after ten iterations, which converge more rapidly than those of (A), (B), and (C). As the value of SADE for (A) gradually decreases from the first to eighteenth iteration, and then, the value rapidly increases from 2.77 mm to 5.59 mm from the thirtieth iteration. The DSM failure is possibly caused by the failure of the constraint of the external force. In addition, (C), (D), and (E) are substantially robust for liver segmentation, which yielded steady segmentation results for the final 15 iterations. Moreover, the final segmentation results of SADE for (C), (D), and (E) are 1.53 mm, 2.08 mm, and 1.50 mm, respectively. Evidently, with the introduction of the constraint conditions, the segmentation results are more accurate and the algorithms converge faster. Hence, it can be concluded that the proposed AMEM is considerably effective for the segmentation of the liver.


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)

Comparison of the segmentation accuracies for different methods.(A) the conventional DSM; (B) DSM constraint by gradient image; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4358832&req=5

pone.0118064.g008: Comparison of the segmentation accuracies for different methods.(A) the conventional DSM; (B) DSM constraint by gradient image; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition.
Mentions: To validate the performance of the main procedures on the final segmentation results of the proposed AMEM, the segmentation results over each iteration step for the combined effects of the key individual processing techniques are compared and evaluated. In addition, the comparison models are designed as follows: (A) the conventional DSM; (B) DSM constraint by gradient force; (C) DSM constraint by the gradient and binary forces; (D) DSM constraint by gradient, binary, and balloon forces; and (E) AMEM: DSM constraint by gradient, binary, balloon forces, and with adaptive triangular facet decomposition. The segmentation error is quantified as the spacing average distance error (SADE) of all the vertices between the deformable model and the true value. Fig. 8 compares the average space errors at every iteration step for the above-mentioned models. The left figure shows the calculation errors at all the iteration steps, and the right figure shows the magnified view of the last 10 iterations. The segmentation errors gradually decreases with the iteration applied from the first to the eighteenth iterations in all six methods. Furthermore, the SADE values for (D) and (E) rapidly decrease from 19.3 mm to 2.49 mm, and 2.79 mm after ten iterations, which converge more rapidly than those of (A), (B), and (C). As the value of SADE for (A) gradually decreases from the first to eighteenth iteration, and then, the value rapidly increases from 2.77 mm to 5.59 mm from the thirtieth iteration. The DSM failure is possibly caused by the failure of the constraint of the external force. In addition, (C), (D), and (E) are substantially robust for liver segmentation, which yielded steady segmentation results for the final 15 iterations. Moreover, the final segmentation results of SADE for (C), (D), and (E) are 1.53 mm, 2.08 mm, and 1.50 mm, respectively. Evidently, with the introduction of the constraint conditions, the segmentation results are more accurate and the algorithms converge faster. Hence, it can be concluded that the proposed AMEM is considerably effective for the segmentation of the liver.

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