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Related Experiment Video

Updated: Dec 21, 2025

Three-dimensional Rendering and Analysis of Immunolabeled, Clarified Human Placental Villous Vascular Networks

Three-dimensional Rendering and Analysis of Immunolabeled, Clarified Human Placental Villous Vascular Networks

Published on: March 29, 2018

Placental Flattening via Volumetric Parameterization.

S Mazdak Abulnaga¹, Esra Abaci Turk², Mikhail Bessmeltsev³

¹Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|May 21, 2020

Summary

This study introduces a new algorithm to flatten 3D placenta shapes for better visualization of pregnancy health. The method improves understanding of placental anatomy and function during pregnancy.

Keywords:

Anatomy visualization Fetal MRI Flattening Injective maps Placenta Volumetric mesh parameterization

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Last Updated: Dec 21, 2025

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Published on: March 29, 2018

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Area of Science:

Medical Imaging
Computational Anatomy
Pregnancy Research

Background:

Placental shape variability poses challenges for in vivo visualization and functional assessment.
Accurate monitoring of placental function is crucial for improving pregnancy outcomes.
Existing methods struggle with the complex, curved anatomy of the in vivo placenta.

Purpose of the Study:

To develop a computational method for flattening the in vivo placenta to a canonical template.
To improve the visualization and interpretation of placental anatomy and function.
To enable more effective pregnancy assessment through enhanced placental imaging.

Main Methods:

A volumetric mesh-based algorithm was developed to map the in vivo placenta shape to a flattened template.
The mapping minimizes symmetric Dirichlet energy to control volumetric distortion.
Constrained line search during gradient descent ensures local injectivity.

Main Results:

The algorithm achieved sub-voxel accuracy in mapping placental boundaries to the template.
Volumetric distortion was effectively controlled across all tested placenta shapes.
The flattening process successfully enhanced the visualization of placental anatomy and function.

Conclusions:

The proposed placenta flattening algorithm provides a robust method for improving in vivo visualization.
This technique aids in the interpretation of placental anatomy and function, potentially improving pregnancy care.
The freely available implementation facilitates further research and clinical application.