ArXiv Preprint
This paper aims to tackle the issues on unavailable or insufficient clinical
US data and meaningful annotation to enable bone segmentation and registration
for US-guided spinal surgery. While the US is not a standard paradigm for
spinal surgery, the scarcity of intra-operative clinical US data is an
insurmountable bottleneck in training a neural network. Moreover, due to the
characteristics of US imaging, it is difficult to clearly annotate bone
surfaces which causes the trained neural network missing its attention to the
details. Hence, we propose an In silico bone US simulation framework that
synthesizes realistic US images from diagnostic CT volume. Afterward, using
these simulated bone US we train a lightweight vision transformer model that
can achieve accurate and on-the-fly bone segmentation for spinal sonography. In
the validation experiments, the realistic US simulation was conducted by
deriving from diagnostic spinal CT volume to facilitate a radiation-free
US-guided pedicle screw placement procedure. When it is employed for training
bone segmentation task, the Chamfer distance achieves 0.599mm; when it is
applied for CT-US registration, the associated bone segmentation accuracy
achieves 0.93 in Dice, and the registration accuracy based on the segmented
point cloud is 0.13~3.37mm in a complication-free manner. While bone US images
exhibit strong echoes at the medium interface, it may enable the model
indistinguishable between thin interfaces and bone surfaces by simply relying
on small neighborhood information. To overcome these shortcomings, we propose
to utilize a Long-range Contrast Learning Module to fully explore the
Long-range Contrast between the candidates and their surrounding pixels.
Ang Li, Jiayi Han, Yongjian Zhao, Keyu Li, Li Liu
2023-01-05
