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Cheng Endowment"},{"name":"Richard T. Cheng Endowment"},{"name":"Dominion scholar fellowship of Old Dominion University"},{"name":"Dominion scholar fellowship of Old Dominion University"},{"name":"Virginia\u2019s Commonwealth Technology Research Fund"},{"name":"Virginia\u2019s Commonwealth Technology Research Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Engineering with Computers"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh\u2019s fidelity. The deformation scheme builds upon the ITK open-source library and is based on the concept of energy minimization, relying on a multi-material point-based registration. It uses non-connectivity patterns to implicitly control the number of extracted feature points needed for the registration and, thus, adjusts the trade-off between the achieved mesh fidelity and the deformation speed. We compare CBC3D with four widely used and state-of-the-art homegrown image-to-mesh conversion methods from industry and academia. Results indicate that the CBC3D meshes: (1) achieve high fidelity, (2) keep the element count reasonably low, and (3) exhibit good element quality.<\/jats:p>","DOI":"10.1007\/s00366-024-02023-w","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T11:02:38Z","timestamp":1722510158000},"page":"3979-4005","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Image-to-mesh conversion method for multi-tissue medical image computing simulations"],"prefix":"10.1007","volume":"40","author":[{"given":"Fotis","family":"Drakopoulos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Garner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikos","family":"Chrisochoides","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"2023_CR1","doi-asserted-by":"publisher","unstructured":"Archip N, Fedorov A, Lloyd B, Chrisochoides N, Golby A, Black PM, Warfield, SK (2006) Integration of patient specific modeling and advanced image processing techniques for image-guided neurosurgery. 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