黑暗中的形状补全：从3D超声中补全椎骨形态
Shape Completion in the Dark: Completing Vertebrae Morphology from 3D Ultrasound
AI总结：
针对3D超声图像解剖结构显示不全的问题，提出基于点云的概率深度学习方法，结合超声物理特性生成合成数据实现椎骨形状补全，保留关键解剖标志，在合成与真实数据上均取得良好效果。
摘要：
目的：超声（US）成像虽具备无辐射的优势，但由于仅能部分显示器官且缺乏完整3D信息，解读难度较高。因此，医疗专业人员在开展基于超声的诊断或检查时，会在脑海中构建3D解剖结构的图谱。本研究旨在复现这一过程，增强解剖结构的可视化呈现。\n方法：我们提出一种基于点云的概率深度学习（DL）方法，通过3D形状补全修复被遮挡的解剖结构，并选择基于超声的脊柱检查作为应用场景。为支撑模型训练，我们通过模拟超声物理特性并考虑固有伪影，生成了部分遮挡脊柱视图的合成3D表征。\n结果：所提模型在合成数据和患者数据上表现一致，Chamfer距离（CD）的均值和中位数差异分别为2.02和0.03。消融实验验证了基于超声物理的数据生成方式的重要性——未采用该方式时，CD均值和中位数差异分别高达11.8和9.55。此外，我们证实3D补全结果保留了解剖标志点，例如棘突（重建CD为4.73）和关节突关节（与真值（GT）的平均距离为4.96mm）。\n结论：本研究证实了腰椎3D形状补全的可行性，能够保留节段层面的特征，且实现了从合成数据到真实数据的成功泛化。融入超声物理特性有助于提升患者数据补全的准确性。值得注意的是，我们的方法保留了关键解剖标志点，并能在正确位置重建关键注射位点。生成的数据和源代码将公开发布（https://github.com/miruna20/Shape-Completion-in-the-Dark）。
英文摘要：
Purpose: Ultrasound (US) imaging, while advantageous for its radiation-free nature, is challenging to interpret due to only partially visible organs and a lack of complete 3D information. While performing US-based diagnosis or investigation, medical professionals therefore create a mental map of the 3D anatomy. In this work, we aim to replicate this process and enhance the visual representation of anatomical structures. Methods: We introduce a point-cloud-based probabilistic DL method to complete occluded anatomical structures through 3D shape completion and choose US-based spine examinations as our application. To enable training, we generate synthetic 3D representations of partially occluded spinal views by mimicking US physics and accounting for inherent artifacts. Results: The proposed model performs consistently on synthetic and patient data, with mean and median differences of 2.02 and 0.03 in CD, respectively. Our ablation study demonstrates the importance of US physics-based data generation, reflected in the large mean and median difference of 11.8 CD and 9.55 CD, respectively. Additionally, we demonstrate that anatomic landmarks, such as the spinous process (with reconstruction CD of 4.73) and the facet joints (mean distance to GT of 4.96mm) are preserved in the 3D completion. Conclusion: Our work establishes the feasibility of 3D shape completion for lumbar vertebrae, ensuring the preservation of level-wise characteristics and successful generalization from synthetic to real data. The incorporation of US physics contributes to more accurate patient data completions. Notably, our method preserves essential anatomic landmarks and reconstructs crucial injections sites at their correct locations. The generated data and source code will be made publicly available (https://github.com/miruna20/Shape-Completion-in-the-Dark).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
