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| 001 | 617302 | ||
| 005 | 20250723172709.0 | ||
| 024 | 7 | _ | |a 10.1117/12.3028465 |2 doi |
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| 100 | 1 | _ | |a Irvine, Sarah C. |0 P:(DE-H253)PIP1099915 |b 0 |
| 111 | 2 | _ | |a Developments in X-Ray Tomography XV |c San Diego |d 2024-08-18 - 2024-08-23 |w United States |
| 245 | _ | _ | |a Multi-modal image registration and machine learning for the generation of 3D virtual histology of bone implants |
| 260 | _ | _ | |a Bellingham, Wash. |c 2024 |b SPIE |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a In our correlative characterisation studies of biodegradable and permanent metal bone implants, we have performed both synchrotron-radiation microtomography (SR-μCT) and histology on the same samples. Histological staining is still the gold standard for tissue visualisation yet requires multiple time-consuming sample preparation steps (fixing, embedding, sectioning and staining) before imaging is performed on individual slices, in contrast to the non-invasive and 3D nature of tomography. In the process of correlating the corresponding data sets, we are able to combine advantages of both modalities by using machine learning methods to generate artificially stained 3D virtual histology datasets from SR-μCT datasets. For this we have developed an automated registration tool to find and fit the correct virtual tomographic plane to each histology slice. Preliminary results are promising after training a modified cycle generative adversarial network on our data, with two different histological stainings. |
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| 700 | 1 | _ | |a Lucas, Christian |0 P:(DE-H253)PIP1093527 |b 1 |
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| 700 | 1 | _ | |a Zeller-Plumhoff, Berit |0 P:(DE-H253)PIP1031548 |b 4 |
| 700 | 1 | _ | |a Moosmann, Julian P. |0 P:(DE-H253)PIP1030371 |b 5 |e Corresponding author |
| 700 | 1 | _ | |a Müller, Bert |0 P:(DE-H253)PIP1008015 |b 6 |e Editor |
| 700 | 1 | _ | |a Wang, Ge |b 7 |e Editor |
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| 999 | C | 5 | |a 10.1007/s11307-018-1246-3 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |a 10.1093/micmic/ozad082 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |a 10.1016/j.neuroimage.2016.06.005 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |a 10.1016/j.bioactmat.2021.10.041 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |1 Jonas Albers |y 2021 |2 Crossref |o Jonas Albers 2021 |
| 999 | C | 5 | |a 10.3389/fgstr.2023.1283052 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |1 Johanna Reiser |y 2024 |2 Crossref |o Johanna Reiser 2024 |
| 999 | C | 5 | |a 10.1088/1361-6560/acba74 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |1 Igor Zingman |y 2024 |2 Crossref |o Igor Zingman 2024 |
| 999 | C | 5 | |1 Jun-Yan Zhu |y 2017 |2 Crossref |t Proceedings of the IEEE international conference on computer vision |o Jun-Yan Zhu Proceedings of the IEEE international conference on computer vision 2017 |
| 999 | C | 5 | |a 10.1002/adem.v23.11 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |a 10.1016/j.bioactmat.2023.05.006 |9 -- missing cx lookup -- |2 Crossref |
| 999 | C | 5 | |a 10.1016/j.bioactmat.2023.07.017 |9 -- missing cx lookup -- |2 Crossref |
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| 999 | C | 5 | |2 Crossref |u https://github.com/eriklindernoren/PyTorch-GAN/tree/master/implementations/cyclegan |
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