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.
翻译:本文旨在解决美国临床数据不全或不足的临床数据以及美国脊髓外科手术注册注册注册注册不全的问题。 虽然美国不是脊椎外科手术的标准模式,但美国临床临床临床数据稀缺是神经网络培训中不可逾越的瓶颈。 此外,由于美国成像的特征,很难明确说明导致经过训练的神经神经网络忽视细节的骨质表面问题。因此,我们提议了一个在硅骨中层的美国模拟模拟框架,将诊断CT数量中现实的美国图像综合起来。之后,我们用这些模拟的美国骨质外科手术培训了一个轻量的视觉变异模型,可以实现脊髓外科外科手术的准确性。在验证实验中,美国通过诊断性脊髓外科外科外科手术量来进行现实的模拟,以促进无辐射导导导导导的螺旋螺旋螺旋螺旋螺旋管配置程序。当用于培训骨质分解分解任务时,Chamfer的距离将达到0.59毫米之间;当用于小的CT注册时,我们使用一个能够实现的骨质骨质骨质变变变变的镜模型,同时,我们在深度的骨质骨质上显示的深度平段上显示的深度平段上显示的深度的直径直路路路路段,这些直路段将显示的直路路段,而以0.0.0.13在以显示的直路段的直路段为以显示的直路。