Book chapter · Conference paper
3D Reconstruction and Segmentation of Dissection Photographs for MRI-Free Neuropathology
University College London1
University of Washington2
Department of Health Technology, Technical University of Denmark3
Magnetic Resonance, Department of Health Technology, Technical University of Denmark4
Medical Image Computing, Magnetic Resonance, Department of Health Technology, Technical University of Denmark5
Biomedical Engineering, Department of Health Technology, Technical University of Denmark6
Massachusetts General Hospital/Harvard Medical School7
Massachusetts Institute of Technology8
Neuroimaging to neuropathology correlation (NTNC) promis-es to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requires a volumetric MRI scan, acquired either ex vivo or a short time prior to death. Unfortunately, ex vivo MRI is difficult and costly, and recent premortem scans of sufficient quality are seldom available.
To bridge this gap, we present methodology to 3D reconstruct and segment full brain image volumes from brain dissection photographs, which are routinely acquired at many brain banks and neuropathology departments. The 3D reconstruction is achieved via a joint registration framework, which uses a reference volume other than MRI.
This volume may represent either the sample at hand (e.g., a surface 3D scan) or the general population (a probabilistic atlas). In addition, we present a Bayesian method to segment the 3D reconstructed photographic volumes into 36 neuroanatomical structures, which is robust to nonuniform brightness within and across photographs.
We evaluate our methods on a dataset with 24 brains, using Dice scores and volume correlations. The results show that dissection photography is a valid replacement for ex vivo MRI in many volumetric analyses, opening an avenue for MRI-free NTNC, including retrospective data. The code is available at https://github.com/htregidgo/DissectionPhotoVolumes.
Language: | English |
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Publisher: | Springer |
Year: | 2020 |
Pages: | 204-214 |
Proceedings: | 23rd International Conference on Medical Image Computing and Computer Assisted Intervention |
Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Journal subtitle: | 23rd International Conference Lima, Peru, October 4–8, 2020 Proceedings, Part V |
ISBN: | 3030597210 , 3030597229 , 9783030597214 and 9783030597221 |
ISSN: | 16113349 and 03029743 |
Types: | Book chapter and Conference paper |
DOI: | 10.1007/978-3-030-59722-1_20 |
ORCIDs: | Van Leemput, Koen |