基于类平衡损失的咽喉反流语义分割与诊断方法

Semantic Segmentation and Diagnosis of Laryngopharyngeal Reflux Based on Class-Balanced Loss

当前咽喉反流疾病的筛查主要依靠电子喉镜图像通过反流体征评分(RFS)量表进行评分,尽管这种量化评估方式增强了筛查诊断的客观性,但误诊率、筛查效率仍有待进一步改进。通过深度学习算法实现基于RFS的咽喉反流量化辅助评估。首先,提出一种基于类平衡损失的咽喉反流语义分割与诊断(CBD-FCN)算法,通过RFS量表先验知识对电子喉镜图像进行喉部多区域语义分割,该算法有效解决了数据集样本类别不平衡和小目标检测的问题,平均交并比(IoU)提高6.38个百分点,声带沟、肉芽肿和黏液等小目标检出率分别提升4个百分点、18个百分点和75个百分点。其次,针对RFS量表中难量化评估的主观项,通过SE-ResNet和目标区域分割特征进行量化并实现评分。上述辅助评分结果可以有效快速地实现咽喉反流的筛查诊断,实验结果表明,所提方法的诊断正确率达到94.40%。该研究不仅提供了一种创新的计算机辅助咽喉反流量化评估方法,同时也为基于RFS量表的咽喉反流评估提供了诊断参考,有助于咽喉反流相关疾病的研究。

Presently,an electronic laryngoscope image is used to assess the severity of laryngopharyngeal reflux disease based on the reflux finding score(RFS)scale.This quantitative evaluation method increases the screening diagnosis objectivity.However,its misdiagnosis rate is high,and screening efficiency is moderate.An anti-flow-aided evaluation of the throat region based on RFS can be implemented using a deep learning algorithm.We propose a semantic segmentation algorithm for diagnosing laryngopharyngeal reflux disease based on existing knowledge of the RFS scale to segment the throat multi-region semantics in an electronic laryngoscope image.This algorithm resolves the problems of unbalanced sample categories and small target detection in the dataset used for this study.The intersection over union ratio for the dataset increased by 6.38 percentage points.Moreover,detection rates for small targets,such as voiceband groove,granuloma,and mucus,increased by 4 percentage points,18 percentage points,and 75 percentage points,respectively.Furthermore,SE-ResNet and target area segmentation are used to quantify and evaluate the subjective items in the RFS scale.Thus,the auxiliary evaluation results aid in rapid and effective diagnosis of laryngopharyngeal reflux.The diagnostic accuracy of the proposed method is 94.40%.This study provides an innovative computer-aided assessment method for throat regurgitation that can be used for diagnostic reference based on the RFS scale.Hence,this study lays foundation for further research into throat regurgitation-related diseases.