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000617302 1112_ $$aDevelopments in X-Ray Tomography XV$$cSan Diego$$d2024-08-18 - 2024-08-23$$wUnited States
000617302 245__ $$aMulti-modal image registration and machine learning for the generation of 3D virtual histology of bone implants
000617302 260__ $$aBellingham, Wash.$$bSPIE$$c2024
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000617302 520__ $$aIn our correlative characterisation studies of biodegradable and permanent metal bone implants, we have performed both synchrotron-radiation microtomography (SR-μCT) and histology on the same samples. Histological staining is still the gold standard for tissue visualisation yet requires multiple time-consuming sample preparation steps (fixing, embedding, sectioning and staining) before imaging is performed on individual slices, in contrast to the non-invasive and 3D nature of tomography. In the process of correlating the corresponding data sets, we are able to combine advantages of both modalities by using machine learning methods to generate artificially stained 3D virtual histology datasets from SR-μCT datasets. For this we have developed an automated registration tool to find and fit the correct virtual tomographic plane to each histology slice. Preliminary results are promising after training a modified cycle generative adversarial network on our data, with two different histological stainings.
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000617302 7001_ $$0P:(DE-H253)PIP1093527$$aLucas, Christian$$b1
000617302 7001_ $$0P:(DE-H253)PIP1107099$$aBootbool, Moral$$b2
000617302 7001_ $$0P:(DE-H253)PIP1020108$$aGalli, Silvia$$b3
000617302 7001_ $$0P:(DE-H253)PIP1031548$$aZeller-Plumhoff, Berit$$b4
000617302 7001_ $$0P:(DE-H253)PIP1030371$$aMoosmann, Julian P.$$b5$$eCorresponding author
000617302 7001_ $$0P:(DE-H253)PIP1008015$$aMüller, Bert$$b6$$eEditor
000617302 7001_ $$aWang, Ge$$b7$$eEditor
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000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1007/s11307-018-1246-3
000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1093/micmic/ozad082
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000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.bioactmat.2021.10.041
000617302 999C5 $$1Jonas Albers$$2Crossref$$oJonas Albers 2021$$y2021
000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.3389/fgstr.2023.1283052
000617302 999C5 $$1Johanna Reiser$$2Crossref$$oJohanna Reiser 2024$$y2024
000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1088/1361-6560/acba74
000617302 999C5 $$1Igor Zingman$$2Crossref$$oIgor Zingman 2024$$y2024
000617302 999C5 $$1Jun-Yan Zhu$$2Crossref$$oJun-Yan Zhu Proceedings of the IEEE international conference on computer vision 2017$$tProceedings of the IEEE international conference on computer vision$$y2017
000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1002/adem.v23.11
000617302 999C5 $$2Crossref$$9-- missing cx lookup --$$a10.1016/j.bioactmat.2023.05.006
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000617302 999C5 $$2Crossref$$uhttps://github.com/eriklindernoren/PyTorch-GAN/tree/master/implementations/cyclegan