Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Aguirre, Marina Crespo Williams-Ramirez, Jonathan Zemlyanker, Dina Hu, Xiaoling Deden-Binder, Lucas J. Herisse, Rogeny Montine, Mark Connors, Theresa R. Mount, Christopher MacDonald, Christine L. Keene, C. Dirk Latimer, Caitlin S. Oakley, Derek H. Hyman, Bradley T. Aguila, Ana Lawry Iglesias, Juan Eugenio |
| author_facet | Aguirre, Marina Crespo Williams-Ramirez, Jonathan Zemlyanker, Dina Hu, Xiaoling Deden-Binder, Lucas J. Herisse, Rogeny Montine, Mark Connors, Theresa R. Mount, Christopher MacDonald, Christine L. Keene, C. Dirk Latimer, Caitlin S. Oakley, Derek H. Hyman, Bradley T. Aguila, Ana Lawry Iglesias, Juan Eugenio |
| contents | Neuropathological analyses benefit from spatially precise volumetric reconstructions that enhance anatomical delineation and improve morphometric accuracy. Our prior work has shown the feasibility of reconstructing 3D brain volumes from 2D dissection photographs. However these outputs sometimes exhibit coarse, overly smooth reconstructions of structures, especially under high anisotropy (i.e., reconstructions from thick slabs). Here, we introduce a computationally efficient super-resolution step that imputes slices to generate anatomically consistent isotropic volumes from anisotropic 3D reconstructions of dissection photographs. By training on domain-randomized synthetic data, we ensure that our method generalizes across dissection protocols and remains robust to large slab thicknesses. The imputed volumes yield improved automated segmentations, achieving higher Dice scores, particularly in cortical and white matter regions. Validation on surface reconstruction and atlas registration tasks demonstrates more accurate cortical surfaces and MRI registration. By enhancing the resolution and anatomical fidelity of photograph-based reconstructions, our approach strengthens the bridge between neuropathology and neuroimaging. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00669 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation Aguirre, Marina Crespo Williams-Ramirez, Jonathan Zemlyanker, Dina Hu, Xiaoling Deden-Binder, Lucas J. Herisse, Rogeny Montine, Mark Connors, Theresa R. Mount, Christopher MacDonald, Christine L. Keene, C. Dirk Latimer, Caitlin S. Oakley, Derek H. Hyman, Bradley T. Aguila, Ana Lawry Iglesias, Juan Eugenio Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics J.3; I.2.10; I.4.5; I.4.6 Neuropathological analyses benefit from spatially precise volumetric reconstructions that enhance anatomical delineation and improve morphometric accuracy. Our prior work has shown the feasibility of reconstructing 3D brain volumes from 2D dissection photographs. However these outputs sometimes exhibit coarse, overly smooth reconstructions of structures, especially under high anisotropy (i.e., reconstructions from thick slabs). Here, we introduce a computationally efficient super-resolution step that imputes slices to generate anatomically consistent isotropic volumes from anisotropic 3D reconstructions of dissection photographs. By training on domain-randomized synthetic data, we ensure that our method generalizes across dissection protocols and remains robust to large slab thicknesses. The imputed volumes yield improved automated segmentations, achieving higher Dice scores, particularly in cortical and white matter regions. Validation on surface reconstruction and atlas registration tasks demonstrates more accurate cortical surfaces and MRI registration. By enhancing the resolution and anatomical fidelity of photograph-based reconstructions, our approach strengthens the bridge between neuropathology and neuroimaging. Our method is publicly available at https://surfer.nmr.mgh.harvard.edu/fswiki/mri_3d_photo_recon |
| title | Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Medical Physics J.3; I.2.10; I.4.5; I.4.6 |
| url | https://arxiv.org/abs/2602.00669 |