School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China; Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, 200050, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, T6G 2R7, Canada.
Med Image Anal. 2024 Dec;98:103305. doi: 10.1016/j.media.2024.103305. Epub 2024 Aug 19.
Three-dimensional (3D) freehand ultrasound (US) is a widely used imaging modality that allows non-invasive imaging of medical anatomy without radiation exposure. Surface reconstruction of US volume is vital to acquire the accurate anatomical structures needed for modeling, registration, and visualization. However, traditional methods cannot produce a high-quality surface due to image noise. Despite improvements in smoothness, continuity, and resolution from deep learning approaches, research on surface reconstruction in freehand 3D US is still limited. This study introduces FUNSR, a self-supervised neural implicit surface reconstruction method to learn signed distance functions (SDFs) from US volumes. In particular, FUNSR iteratively learns the SDFs by moving the 3D queries sampled around volumetric point clouds to approximate the surface, guided by two novel geometric constraints: sign consistency constraint and on-surface constraint with adversarial learning. Our approach has been thoroughly evaluated across four datasets to demonstrate its adaptability to various anatomical structures, including a hip phantom dataset, two vascular datasets and one publicly available prostate dataset. We also show that smooth and continuous representations greatly enhance the visual appearance of US data. Furthermore, we highlight the potential of our method to improve segmentation performance, and its robustness to noise distribution and motion perturbation.
三维（3D）自由式超声（US）是一种广泛使用的成像方式，它允许在不暴露于辐射的情况下对医学解剖结构进行非侵入性成像。US 体积的表面重建对于获取建模、配准和可视化所需的准确解剖结构至关重要。然而，由于图像噪声，传统方法无法生成高质量的表面。尽管深度学习方法在平滑度、连续性和分辨率方面有所改进，但自由式 3D US 中的表面重建研究仍然有限。本研究介绍了 FUNSR，这是一种自监督的神经隐式表面重建方法，用于从 US 体积中学习有符号距离函数（SDF）。具体来说，FUNSR 通过移动围绕体素点云采样的 3D 查询来迭代学习 SDF，以近似表面，这受到两个新颖的几何约束的指导：符号一致性约束和带有对抗学习的表面上约束。我们的方法已经在四个数据集上进行了全面评估，以证明其对各种解剖结构的适应性，包括髋关节模型数据集、两个血管数据集和一个公开的前列腺数据集。我们还表明，平滑和连续的表示极大地增强了 US 数据的视觉外观。此外，我们强调了我们的方法在提高分割性能方面的潜力，以及对噪声分布和运动干扰的鲁棒性。
