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3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology

Tregidgo, H; Casamitjana, A; Latimer, C; Kilgore, M; Robinson, ED; Blackburn, E; Leemput, KV; ... Iglesias, JE; + view all (2020) 3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology. In: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing Green open access

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Abstract

Neuroimaging to neuropathology correlation (NTNC) promises 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.

Type: Proceedings paper
Title: 3D Reconstruction and Segmentation of Dissection Photographs for MRI-free Neuropathology
Event: 23rd International Conference on Medical Image Computing & Computer Assisted Intervention
ISBN-13: 978-3-030-59721-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59722-1_20
Publisher version: https://doi.org/10.1007/978-3-030-59722-1_20
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10112564
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