title: Adaptive Mesh Expansion Model (AMEM) for liver segmentation from CT image.
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.
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Xuehu
authors.initials: X
authors.lastname: Wang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Danni
authors.initials: D
authors.lastname: Ai
authors.affiliation: Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
authors.forename: Yongchang
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Songyuan
authors.initials: S
authors.lastname: Tang
authors.affiliation: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Yongtian
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging*; D008962:Models, Theoretical; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods