2021/11/15 by S. Mazdak Abulnaga, Esra Abacı Türk, Esra Abaci Turk +10 · 1 citation
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fetal and Pediatric Neurological Disorders #MRI in cancer diagnosis #Pregnancy and preeclampsia studies #cs.CV
paper · pdf · doi:10.48550/arxiv.2111.07900
Accepted to IEEE TMI ( (c) IEEE). This manuscript expands the MICCAI 2019 paper (arXiv:1903.05044) by developing additional template models and extensions to improve robustness, expanded evaluation on a significantly larger dataset, and experiments and discussion demonstrating utility for clinical research. Code is available at https://github.com/mabulnaga/placenta-flattening
arxiv created 2021/11/15 · openalex publication_date 2021/11/15 · arxiv updated 2021/11/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We present a volumetric mesh-based algorithm for parameterizing the placenta to a flattened template to enable effective visualization of local anatomy and function. MRI shows potential as a research tool as it provides signals directly related to placental function. However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult. We address interpretation challenges by mapping the placenta so that it resembles the familiar ex vivo shape. We formulate the parameterization as an optimization problem for mapping the placental shape represented by a volumetric mesh to a flattened template. We employ the symmetric Dirichlet energy to control local distortion throughout the volume. Local injectivity in the mapping is enforced by a constrained line search during the gradient descent optimization. We validate our method using a research study of 111 placental shapes extracted from BOLD MRI images. Our mapping achieves sub-voxel accuracy in matching the template while maintaining low distortion throughout the volume. We demonstrate how the resulting flattening of the placenta improves visualization of anatomy and function. Our code is freely available at https://github.com/mabulnaga/placenta-flattening .