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20232026
most citedDynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Aligning Fetal Anatomy with Kinematic Tree Log-Euclidean PolyRigid Transforms

Yingcheng Liu, Athena Taymourtash, Yang Liu +5

Automated analysis of articulated bodies is crucial in medical imaging. Existing surface-based models often ignore internal volumetric structures and rely on deformation methods th…

cs.CV2025

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

Yingcheng Liu, Peiqi Wang, Sebastian Diaz +4

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…

cs.CL2024

Calibrating Expressions of Certainty

Peiqi Wang, Barbara D. Lam, Yingcheng Liu +5

We present a novel approach to calibrating linguistic expressions of certainty, e.g., "Maybe" and "Likely". Unlike prior work that assigns a single score to each certainty phrase,…

eess.IV2023★ 3 cited

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

Zeen Chi, Zhongxiao Cong, Clinton J. Wang +6

We present a method for fast biomedical image atlas construction using neural fields. Atlases are key to biomedical image analysis tasks, yet conventional and deep network estimati…

cs.CV2023

Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series

Yingcheng Liu, Neerav Karani, Neel Dey +5

The placenta plays a crucial role in fetal development. Automated 3D placenta segmentation from fetal EPI MRI holds promise for advancing prenatal care. This paper proposes an effe…