标题： 从切片到结构：从徒手经阴道超声中无监督的女性骨盆解剖三维重建 显示英文标题

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

摘要： 容积超声有潜力显著提高诊断准确性和临床决策，但其广泛采用仍受限于对专用硬件和限制性采集协议的依赖。 在本工作中，我们提出了一种新颖的无监督框架，用于从自由手2D经阴道超声（TVS）扫视中重建3D解剖结构，而无需外部跟踪或学习的姿态估计器。 我们的方法将高斯点云的原理应用于超声领域，引入了一种切片感知的、可微分的光栅化器，专门针对超声成像的独特物理和几何特性。 我们将解剖结构建模为各向异性3D高斯分布的集合，并直接从图像级监督优化其参数，利用无传感器的探头运动估计和领域特定的几何先验。 结果是一个紧凑、灵活且内存高效的容积表示，能够以高空间保真度捕捉解剖细节。 这项工作表明，可以通过纯粹的计算手段实现从2D超声图像的准确3D重建，为传统3D系统提供了一种可扩展的替代方案，并为人工智能辅助分析和诊断开辟了新机遇。 显示英文摘要

摘要： 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 (TVS) sweeps, without requiring external tracking or learned pose estimators. Our method 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, leveraging sensorless probe motion estimation and domain-specific geometric priors. 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.