Placental Flattening via Volumetric Parameterization
Name
1903.05044.pdf
Description
Submitted version
Size
2.63 MB
Format
Adobe PDF
Checksum (MD5)
da5ee9f2eaf4c1f12a4aeb33972027e7
Author(s)
Abulnaga, Sayed Mazdak
Abaci Turk, Esra
Bessmeltsev, Mikhail
Grant, P. Ellen
Solomon, Justin
Golland, Polina
Date Issued
October 2019
Journal
Lecture Notes in Computer Science
Publisher
Springer
Citation
Abulnaga, S. Mazdak et al. "Placental Flattening via Volumetric Parameterization." International Conference on Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, 11767, Springer, 2019, 39-47. © 2019 Springer Nature
Version
Original manuscript
Abstract
We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume. Local injectivity is enforced via constrained line search during gradient descent. We evaluate the proposed method on 28 placenta shapes extracted from MRI images in a clinical study of placental function. We achieve sub-voxel accuracy in mapping the boundary of the placenta to the template while successfully controlling distortion throughout the volume. We illustrate how the resulting mapping of the placenta enhances visualization of placental anatomy and function. Our implementation is freely available at https://github.com/mabulnaga/placenta-flattening.
Description
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11767).
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-32251-9_5
