Department of Radiology, Fujian Children's Hospital (Fujian Branch of Shanghai Children's Medical Center), College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350014, Fujian, China.
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, 350005, Fujian, China.
Sci Rep. 2024 Aug 10;14(1):18619. doi: 10.1038/s41598-024-69827-0.
Adenoid hypertrophy can lead to adenoidal mouth breathing, which can result in "adenoid face" and, in severe cases, can even lead to respiratory tract obstruction. The Fujioka ratio method, which calculates the ratio of adenoid (A) to nasopharyngeal (N) space in an adenoidal-cephalogram (A/N), is a well-recognized and effective technique for detecting adenoid hypertrophy. However, this process is time-consuming and relies on personal experience, so a fully automated and standardized method needs to be designed. Most of the current deep learning-based methods for automatic diagnosis of adenoids are CNN-based methods, which are more sensitive to features similar to adenoids in lateral views and can affect the final localization results. In this study, we designed a local attention-based method for automatic diagnosis of adenoids, which takes AdeBlock as the basic module, fuses the spatial and channel information of adenoids through two-branch local attention computation, and combines the downsampling method without losing spatial information. Our method achieved mean squared error (MSE) 0.0023, mean radial error (MRE) 1.91, and SD (standard deviation) 7.64 on the three hospital datasets, outperforming other comparative methods.
腺样体肥大可导致腺样体口呼吸,进而出现“腺样体面容”,严重者甚至可导致呼吸道梗阻。腺样体-鼻咽腔比率(A/N)是一种公认的有效检测腺样体肥大的方法,通过测量鼻咽侧位片上腺样体(A)与鼻咽腔(N)的比值来计算。然而,该过程耗时且依赖于个人经验,因此需要设计一种全自动且标准化的方法。目前大多数基于深度学习的腺样体自动诊断方法都是基于卷积神经网络(CNN)的方法,这些方法对侧位片上与腺样体相似的特征更为敏感,可能会影响最终的定位结果。在这项研究中,我们设计了一种基于局部注意力的腺样体自动诊断方法,该方法以 AdeBlock 为基本模块,通过两分支局部注意力计算融合腺样体的空间和通道信息,并结合下采样方法,在不损失空间信息的情况下进行融合。我们的方法在三个医院数据集上的均方误差(MSE)为 0.0023,平均半径误差(MRE)为 1.91,标准差(SD)为 7.64,优于其他对比方法。