Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods.

牙科种植手术是一项重要的工具，但设计过程严重依赖于牙医手动模拟种植角度和深度。我们在此论文中受到视频定位任务启发，将种植深度预测简化为视频定位，并开发了一种纹理感知种植深度预测网络（TPNet），用于直接输出种植深度而无需复杂的口腔骨测量。TPNet由种植区域检测器（IRD）和种植深度预测网络（IDPNet）组成。IRD是一个物体检测器，用于从CBCT中裁剪出候选种植物体积，从而大大节省了计算资源。IDPNet采用裁剪后的CBCT数据来预测种植深度。我们设计了一种纹理感知损失（TPL），使IDPNet的编码器能够感知切片之间的纹理变化。对大型牙科种植数据集的大量实验表明，所提出的TPNet优于现有方法的性能。

将植入深度预测简化为视频定位：一种纹理感知的植入深度预测网络