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
(this https URL).

通过点云概率深度学习方法对被遮挡的解剖结构进行 3D 形状完整性补全，并应用于基于超声波的脊柱检查，结果表明该方法在合成数据和实际患者数据上表现一致，保留了重要解剖标志和正确位置的关键注射点。