Zhao Huanmiao, Chen Junhua, Yun Zhaoqiang, Feng Qianjin, Zhong Liming, Yang Wei
School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, 510515, China.
Heliyon. 2023 Jun 28;9(7):e17651. doi: 10.1016/j.heliyon.2023.e17651. eCollection 2023 Jul.
Accurate segmentation of the mandibular canal is essential in dental implant and maxillofacial surgery, which can help prevent nerve or vascular damage inside the mandibular canal. Achieving this is challenging because of the low contrast in CBCT scans and the small scales of mandibular canal areas. Several innovative methods have been proposed for mandibular canal segmentation with positive performance. However, most of these methods segment the mandibular canal based on sliding patches, which may adversely affect the morphological integrity of the tubular structure. In this study, we propose whole mandibular canal segmentation using transformed dental CBCT volume in the Frenet frame. Considering the connectivity of the mandibular canal, we propose to transform the CBCT volume to obtain a sub-volume containing the whole mandibular canal based on the Frenet frame to ensure complete 3D structural information. Moreover, to further improve the performance of mandibular canal segmentation, we use clDice to guarantee the integrity of the mandibular canal structure and segment the mandibular canal. Experimental results on our CBCT dataset show that integrating the proposed transformed volume in the Frenet frame into other state-of-the-art methods achieves a improvement in Dice performance. Our proposed method can achieve impressive results with a Dice value of 0.865 (±0.035), and a clDice value of 0.971 (±0.020), suggesting that our method can segment the mandibular canal with superior performance.
下颌神经管的精确分割在牙种植和颌面外科手术中至关重要，这有助于防止下颌神经管内的神经或血管损伤。由于CBCT扫描中的对比度低以及下颌神经管区域的尺度小，实现这一点具有挑战性。已经提出了几种具有积极效果的下颌神经管分割创新方法。然而，这些方法大多基于滑动补丁分割下颌神经管，这可能会对管状结构的形态完整性产生不利影响。在本研究中，我们提出在弗伦内特标架中使用变换后的牙科CBCT体积进行全下颌神经管分割。考虑到下颌神经管的连通性，我们建议基于弗伦内特标架变换CBCT体积以获得包含整个下颌神经管的子体积，以确保完整的三维结构信息。此外，为了进一步提高下颌神经管分割的性能，我们使用clDice来保证下颌神经管结构的完整性并分割下颌神经管。在我们的CBCT数据集上的实验结果表明，将在弗伦内特标架中提出的变换体积集成到其他先进方法中可实现Dice性能的提升。我们提出的方法可以取得令人印象深刻的结果，Dice值为0.865（±0.035），clDice值为0.971（±0.020），这表明我们的方法能够以卓越的性能分割下颌神经管。
