From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound

TL;DR

This paper introduces an unsupervised computational framework, TVGS, that reconstructs detailed 3D pelvic anatomy from freehand 2D transvaginal ultrasound scans without external tracking, enhancing diagnostic potential.

Contribution

The novel TVGS method adapts Gaussian Splatting principles to ultrasound, modeling anatomy with anisotropic Gaussians and jointly optimizing slice poses and structure without supervision.

Findings

Achieves high-fidelity 3D reconstructions from 2D ultrasound.

Operates without external tracking or pose estimation.

Provides a scalable, memory-efficient volumetric representation.

Abstract

Volumetric ultrasound has the potential to significantly improve diagnostic accuracy and clinical decision-making, yet its widespread adoption remains limited by dependence on specialized hardware and restrictive acquisition protocols. In this work, we present a novel unsupervised framework for reconstructing 3D anatomical structures from freehand 2D transvaginal ultrasound sweeps, without requiring external tracking or learned pose estimators. Our method, TVGS, adapts the principles of Gaussian Splatting to the domain of ultrasound, introducing a slice-aware, differentiable rasterizer tailored to the unique physics and geometry of ultrasound imaging. We model anatomy as a collection of anisotropic 3D Gaussians and optimize their parameters directly from image-level supervision. To ensure robustness against irregular probe motion, we introduce a joint optimization scheme that refines slice poses alongside anatomical structure. The result is a compact, flexible, and memory-efficient volumetric representation that captures anatomical detail with high spatial fidelity. This work demonstrates that accurate 3D reconstruction from 2D ultrasound images can be achieved through purely computational means, offering a scalable alternative to conventional 3D systems and enabling new opportunities for AI-assisted analysis and diagnosis.