000120184 001__ 120184
000120184 005__ 20240319081016.0
000120184 0247_ $$2doi$$a10.3390/app122211557
000120184 0248_ $$2sideral$$a131087
000120184 037__ $$aART-2022-131087
000120184 041__ $$aeng
000120184 100__ $$0(orcid)0000-0001-7452-0437$$aSainz-DeMena, Diego$$uUniversidad de Zaragoza
000120184 245__ $$aIm2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences
000120184 260__ $$c2022
000120184 5060_ $$aAccess copy available to the general public$$fUnrestricted
000120184 5203_ $$aThe 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.
000120184 536__ $$9info:eu-repo/grantAgreement/EC/H2020/826494/EU/PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers/PRIMAGE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 826494-PRIMAGE$$9info:eu-repo/grantAgreement/ES/MCIU/FPU18/04541$$9info:eu-repo/grantAgreement/ES/MICINN-AEI-FEDER/PID2021-122409OB-C21$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113819RB-I00/AEI/10.13039/501100011033$$9info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709/AEI/10.13039/501100011033
000120184 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000120184 590__ $$a2.7$$b2022
000120184 592__ $$a0.492$$b2022
000120184 591__ $$aPHYSICS, APPLIED$$b78 / 160 = 0.488$$c2022$$dQ2$$eT2
000120184 591__ $$aENGINEERING, MULTIDISCIPLINARY$$b42 / 90 = 0.467$$c2022$$dQ2$$eT2
000120184 591__ $$aCHEMISTRY, MULTIDISCIPLINARY$$b100 / 178 = 0.562$$c2022$$dQ3$$eT2
000120184 591__ $$aMATERIALS SCIENCE, MULTIDISCIPLINARY$$b208 / 343 = 0.606$$c2022$$dQ3$$eT2
000120184 593__ $$aFluid Flow and Transfer Processes$$c2022$$dQ2
000120184 593__ $$aMaterials Science (miscellaneous)$$c2022$$dQ2
000120184 593__ $$aEngineering (miscellaneous)$$c2022$$dQ2
000120184 593__ $$aInstrumentation$$c2022$$dQ2
000120184 593__ $$aProcess Chemistry and Technology$$c2022$$dQ3
000120184 593__ $$aComputer Science Applications$$c2022$$dQ3
000120184 594__ $$a4.5$$b2022
000120184 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000120184 700__ $$0(orcid)0000-0002-9864-7683$$aGarcía-Aznar, José Manuel$$uUniversidad de Zaragoza
000120184 700__ $$0(orcid)0000-0002-2901-4188$$aPérez, María Ángeles$$uUniversidad de Zaragoza
000120184 700__ $$0(orcid)0000-0002-3784-1140$$aBorau, Carlos$$uUniversidad de Zaragoza
000120184 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000120184 773__ $$g12, 22 (2022), 11557 [15 pp.]$$pAppl. sci.$$tApplied Sciences (Switzerland)$$x2076-3417
000120184 8564_ $$s8616708$$uhttps://zaguan.unizar.es/record/120184/files/texto_completo.pdf$$yVersión publicada
000120184 8564_ $$s2754457$$uhttps://zaguan.unizar.es/record/120184/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000120184 909CO $$ooai:zaguan.unizar.es:120184$$particulos$$pdriver
000120184 951__ $$a2024-03-18-15:37:24
000120184 980__ $$aARTICLE