120184 20240319081016.0 doi 10.3390/app122211557 sideral 131087 ART-2022-131087 eng Sainz-DeMena, Diego Universidad de Zaragoza (orcid)0000-0001-7452-0437 Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences 2022 Access copy available to the general public Unrestricted The future of personalised medicine lies in the development of increasingly sophisticated digital twins, where the patient-specific data is fed into predictive computational models that support the decisions of clinicians on the best therapies or course actions to treat the patient’s afflictions. The development of these personalised models from image data requires a segmentation of the geometry of interest, an estimation of intermediate or missing slices, a reconstruction of the surface and generation of a volumetric mesh and the mapping of the relevant data into the reconstructed three-dimensional volume. There exist a wide number of tools, including both classical and artificial intelligence methodologies, that help to overcome the difficulties in each stage, usually relying on the combination of different software in a multistep process. In this work, we develop an all-in-one approach wrapped in a Python library called im2mesh that automatizes the whole workflow, which starts reading a clinical image and ends generating a 3D finite element mesh with the interpolated patient data. In this work, we apply this workflow to an example of a patient-specific neuroblastoma tumour. The main advantages of our tool are its straightforward use and its easy integration into broader pipelines. info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541 info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21 info:eu-repo/grantAgreement/ES/MICINN/PID2020-113819RB-I00/AEI/10.13039/501100011033 info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.7 2022 PHYSICS, APPLIED 78 / 160 = 0.488 2022 Q2 T2 ENGINEERING, MULTIDISCIPLINARY 42 / 90 = 0.467 2022 Q2 T2 CHEMISTRY, MULTIDISCIPLINARY 100 / 178 = 0.562 2022 Q3 T2 MATERIALS SCIENCE, MULTIDISCIPLINARY 208 / 343 = 0.606 2022 Q3 T2 0.492 2022 Fluid Flow and Transfer Processes 2022 Q2 Materials Science (miscellaneous) 2022 Q2 Engineering (miscellaneous) 2022 Q2 Instrumentation 2022 Q2 Process Chemistry and Technology 2022 Q3 Computer Science Applications 2022 Q3 4.5 2022 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion García-Aznar, José Manuel Universidad de Zaragoza (orcid)0000-0002-9864-7683 Pérez, María Ángeles Universidad de Zaragoza (orcid)0000-0002-2901-4188 Borau, Carlos Universidad de Zaragoza (orcid)0000-0002-3784-1140 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 12, 22 (2022), 11557 [15 pp.] Appl. sci. Applied Sciences (Switzerland) 2076-3417 8616708 http://zaguan.unizar.es/record/120184/files/texto_completo.pdf Versión publicada 2754457 http://zaguan.unizar.es/record/120184/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:120184 articulos driver 2024-03-18-15:37:24 ARTICLE