Detection of Meige's syndrome based on multi-scale feature extraction and temporal segmentation
10.3969/j.issn.1005-202X.2025.07.018
- VernacularTitle:基于多尺度特征提取和时间分割的梅杰综合征检测
- Author:
Bicao LI
1
;
Benze YI
;
Bei WANG
;
Zhitao LIU
;
Xuwei GUO
;
Yan WANG
Author Information
1. 中原工学院信息与通信工程学院,河南 郑州 450007
- Publication Type:Journal Article
- Keywords:
Meige's syndrome;
temporal action detection;
multi-scale feature extraction;
temporal segmentation
- From:
Chinese Journal of Medical Physics
2025;42(7):962-968
- CountryChina
- Language:Chinese
-
Abstract:
The diagnosis of Meige's syndrome predominantly relies on the clinical assessment by physicians.Given the complexity and similarity of its symptoms to other neurological disorders,the diagnosis is crucial for both doctors and patients.Herein a detection dataset for Meige's syndrome is compiled from video recordings of 31 patients,and an automated diagnostic system for Meige's syndrome(MS-Net)applicable to untrimmed videos is developed.The system utilizes RetinaNet and UNet3+to construct temporal detection and segmentation branches for multi-scale feature extraction and temporal segmentation,obtains probability vectors for detection windows and the probability of disease onset per frame via the decoding of temporal detection and segmentation branches,and finally generates a refined probability for each window by processing the probability predictions from both branches using a multi-layer perceptron.The model performance is optimized using additional loss functions and data augmentation techniques,operating on features interpretable by clinical physicians.MS-Net can assist in the diagnosis of Meige's syndrome,improving the accuracy,convenience,and efficiency of the early diagnosis.The comparison of MS-Net with other state-of-the-art networks indicates that MS-Net achieves comparable performance in terms of average precision while utilizing interpretable features required in clinical practice.
