PDF(Pubmed)
Abstract：
Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts.
摘要：
准确的下牙槽神经（IAN）管分割已被认为是牙科的一项关键任务。未能准确识别IAN管的位置可能会导致牙科手术期间的神经损伤。虽然IAN管道可以从牙科锥形束计算机断层扫描中检测到，由于运河很薄，牙医通常很难准确识别它们，小,跨越许多切片。本文着重于提高IAN运河分割的准确性。通过将我们提出的频域注意机制集成到UNet中，拟议的频率注意UNet（FAUNet）能够在骰子和表面骰子系数中达到75.55％和81.35％，分别,远高于其他竞争方法，只添加224个参数到经典的UNet。与经典的UNet相比，我们提出的FAUNet在骰子系数和表面骰子系数方面实现了2.39%和2.82%的增益，分别。还讨论了在频域中发展注意力的潜在优势，这表明频域注意力机制可以比空间域注意力机制获得更好的性能。
