Optimizing Photogrammetry Parameters for 3D Reconstruction of Fresh Brain Specimens.
Overview
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, Medellín, Colombia
- Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
Abstract
Photogrammetry has become an essential tool in medical and research fields for generating high-fidelity 3D models from 2D images. However, optimizing the imaging and processing parameters remains a challenge, particularly for fresh brain specimens, where repeated imaging is not feasible. This study systematically evaluates the impact of various Metashape alignment settings on 3D reconstruction outcomes, analyzing 12,600 configuration combinations and 63,000 alignments. Our results suggest that the optimal imaging setup consists of four photo sets: two captured at anatomical position (level with the brain and 30 cm above it at a 30° camera tilt) and two identical sets with the basal side facing upwards. The brain should be rotated 3° between shots, generating 120 images per set. Initial processing should be performed without masks using medium-precision alignment in Metashape. If alignment fails, we recommend generating one mask per image, delineating the brain’s borders, and applying masks to key points. Notably, higher image density only improves alignment reliability when masking is selected to detected features and may only increase processing time. We also observed variability in alignment results under identical conditions, suggesting an inherent stochastic component in Metashape. Consequently, unsuccessful alignments should be repeated before modifying imaging parameters. To our knowledge, this is the first study to systematically define an optimal imaging and processing parameters for 3D photogrammetry of fresh brains using a turntable. Future research should focus on determining the minimum number of images required to ensure high-quality reconstructions.
Supplementary Information: The online version contains supplementary material available at 10.1007/
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
No file of the authors' code could be read here: it is described below, and read at its source.
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Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
The paper's code and data availability statement is in the Data section.
Tracing map
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Data
No dataset and no data link were found in the paper.
Data Availability
The brain photogrammetry images, an Excel file containing render data, and the Python scripts used in this study are provided through this link: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.
Version 2, 28 September 2026
- Publisher: n/a → Springer Science+Business Media
- Funding: added Universidad de Antioquia
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 4 keywords, 6 MeSH terms, 28 references.
Cite
This paper
Rueda-Pérez, C. A., Valencia-Loaiza, M.-C., Vega-Cordoba, P., Tobón, J., Anaya, D. S., Gonzalez-Leyton, A., Mazo, J. F., Tarazona, J. D., Ruiz, S., Martinez, J. G., Echeverri-Garcia, A., Gomez-Moreno, C. J., Vicaño-Metaute, B., Gómez-Ramirez, J., & Villegas-Lanau, A. (2026). Optimizing Photogrammetry Parameters for 3D Reconstruction of Fresh Brain Specimens. Neuroinformatics, 24(3), 50. https://
BibTeX
@article{ruedaperez2026o
author = {Rueda-Pérez, Carlos A and Valencia-Loaiza, María-Camila and Vega-Cordoba, Paula and Tobón, Juliana and Anaya, Dylan S and Gonzalez-Leyton, Andres and Mazo, Juan F and Tarazona, Jesús D and Ruiz, Santiago and Martinez, Juan G and Echeverri-Garcia, Andrés and Gomez-Moreno, Catherine J and Vicaño-Metaute, Brian and Gómez-Ramirez, Johana and Villegas-Lanau, Andres},
title = {{Optimizing Photogrammetry Parameters for 3D Reconstruction of Fresh Brain Specimens}},
journal = {Neuroinformatics},
year = {2026},
month = jul,
volume = {24},
number = {3},
pages = {50},
publisher = {Springer Science+Business Media},
issn = {1539-2791},
doi = {10.1007/
url = {https://
pmid = {42501145},
pmcid = {PMC13401559}
}
RIS
TY - JOUR
AU - Rueda-Pérez, Carlos A
AU - Valencia-Loaiza, María-Camila
AU - Vega-Cordoba, Paula
AU - Tobón, Juliana
AU - Anaya, Dylan S
AU - Gonzalez-Leyton, Andres
AU - Mazo, Juan F
AU - Tarazona, Jesús D
AU - Ruiz, Santiago
AU - Martinez, Juan G
AU - Echeverri-Garcia, Andrés
AU - Gomez-Moreno, Catherine J
AU - Vicaño-Metaute, Brian
AU - Gómez-Ramirez, Johana
AU - Villegas-Lanau, Andres
TI - Optimizing Photogrammetry Parameters for 3D Reconstruction of Fresh Brain Specimens
T2 - Neuroinformatics
J2 - Neuroinformatics
PY - 2026
DA - 2026/
VL - 24
IS - 3
SP - 50
SN - 1539-2791
PB - Springer Science+Business Media
DO - 10.1007/
UR - https://
LA - en
ER -
CSL-JSON
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