Fetuses Made Simple: Modeling and Tracking of Fetal Shape and Pose

2025-06-21

AI Summary

Existing fetal MRI analysis methods are limited by oversimplified keypoint representations or challenges in volumetric segmentation under large-scale non-rigid motion. To address this, we propose Fetal-SMPL—the first 3D articulated statistical fetal body model—enabling decoupled modeling of pose and shape for robust joint motion-morphology estimation. Trained on 19,816 fetal MRI volumes, Fetal-SMPL establishes a deformable, standardized 3D fetal template. We design an iterative optimization framework that jointly estimates pose in image space while regressing shape parameters in canonical space, supporting dynamic motion tracking, anatomical visualization, and automated anthropometric measurement. Evaluated on unseen data, our method achieves a surface registration error of 3.2 mm (relative to 3-mm isotropic voxels), significantly improving both motion modeling fidelity and morphological analysis reliability. This work introduces a new paradigm for quantitative prenatal developmental assessment.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Analyzing fetal body motion and shape is paramount in prenatal diagnostics and monitoring. Existing methods for fetal MRI analysis mainly rely on anatomical keypoints or volumetric body segmentations. Keypoints simplify body structure to facilitate motion analysis, but may ignore important details of full-body shape. Body segmentations capture complete shape information but complicate temporal analysis due to large non-local fetal movements. To address these limitations, we construct a 3D articulated statistical fetal body model based on the Skinned Multi-Person Linear Model (SMPL). Our algorithm iteratively estimates body pose in the image space and body shape in the canonical pose space. This approach improves robustness to MRI motion artifacts and intensity distortions, and reduces the impact of incomplete surface observations due to challenging fetal poses. We train our model on segmentations and keypoints derived from $19,816$ MRI volumes across $53$ subjects. Our model captures body shape and motion across time series and provides intuitive visualization. Furthermore, it enables automated anthropometric measurements traditionally difficult to obtain from segmentations and keypoints. When tested on unseen fetal body shapes, our method yields a surface alignment error of $3.2$ mm for $3$ mm MRI voxel size. To our knowledge, this represents the first 3D articulated statistical fetal body model, paving the way for enhanced fetal motion and shape analysis in prenatal diagnostics. The code is available at https://github.com/MedicalVisionGroup/fetal-smpl .

Problem

• Modeling fetal shape and pose for prenatal diagnostics
• Addressing limitations of keypoints and body segmentations in MRI
• Improving robustness to MRI artifacts and incomplete observations

Innovation

• 3D articulated statistical fetal body model
• Iterative pose and shape estimation
• Robust to MRI artifacts and distortions