Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences
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. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/22/11557 |
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| author | Diego Sainz-DeMena José Manuel García-Aznar María Ángeles Pérez Carlos Borau |
| author_facet | Diego Sainz-DeMena José Manuel García-Aznar María Ángeles Pérez Carlos Borau |
| author_sort | Diego Sainz-DeMena |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:30:01Z |
| format | Article |
| id | doaj.art-1b53b98ab8c14854812f3961c03b59dc |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T18:30:01Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1b53b98ab8c14854812f3961c03b59dc2023-11-24T07:37:11ZengMDPI AGApplied Sciences2076-34172022-11-0112221155710.3390/app122211557Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image SequencesDiego Sainz-DeMena0José Manuel García-Aznar1María Ángeles Pérez2Carlos Borau3Multiscale in Mechanical and Biological Engineering, Instituto de Investigación en Ingeniería de Aragón (I3A), University of Zaragoza, 500018 Zaragoza, SpainMultiscale in Mechanical and Biological Engineering, Instituto de Investigación en Ingeniería de Aragón (I3A), University of Zaragoza, 500018 Zaragoza, SpainMultiscale in Mechanical and Biological Engineering, Instituto de Investigación en Ingeniería de Aragón (I3A), University of Zaragoza, 500018 Zaragoza, SpainMultiscale in Mechanical and Biological Engineering, Instituto de Investigación en Ingeniería de Aragón (I3A), University of Zaragoza, 500018 Zaragoza, SpainThe 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.https://www.mdpi.com/2076-3417/12/22/11557python librarymesh generationslice interpolationmedical imagepatient-specific computational modelling |
| spellingShingle | Diego Sainz-DeMena José Manuel García-Aznar María Ángeles Pérez Carlos Borau Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences Applied Sciences python library mesh generation slice interpolation medical image patient-specific computational modelling |
| title | Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences |
| title_full | Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences |
| title_fullStr | Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences |
| title_full_unstemmed | Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences |
| title_short | Im2mesh: A Python Library to Reconstruct 3D Meshes from Scattered Data and 2D Segmentations, Application to Patient-Specific Neuroblastoma Tumour Image Sequences |
| title_sort | im2mesh a python library to reconstruct 3d meshes from scattered data and 2d segmentations application to patient specific neuroblastoma tumour image sequences |
| topic | python library mesh generation slice interpolation medical image patient-specific computational modelling |
| url | https://www.mdpi.com/2076-3417/12/22/11557 |
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