Name
Abstract | ||
|---|---|---|
The quantification of the tear meniscus height can be helpful in the diagnosis of Dry Eyes Disease. This paper presents a method for automatic quantitation of lower tear meniscus height (TMH) with fully convolutional neural networks (FCNN) and investigate its performance and efficacy compared to manual measurements. A total of 485 images from 217 subjects were acquired with a mainstream corneal topographer and then divided these images into the development and testing set respectively. The development set was used to train the FCNN models, while the testing set to evaluate the performance of the models. TMH of each image was assessed by the proposed method based on the corresponding segmentation mask of tear meniscus and compared against the manual results. The tear meniscus of each image in the testing set was segmented by the FCNN. Five-fold cross validation revealed an overall average intersection of Union (IoU) of 82.5 %, a F1-score of 90.1 % for tear meniscus segmentation. The algorithm results of TMH had a higher correlation (r = 0.965, p < 0.001) with the ground-truth compared with the manual obtained results (r = 0.898, p < 0.001). The curve of TMH was plotted to reveal the spatial variation along the lower eyelid from nasal and temporal. Higher TMH were found at nasal (median: 0.26 mm) and temporal (0.27 mm) canthi compared with the locations right under the pupil center (0.19 mm). On the experimental data, the proposed method provided reliable TMH results with a higher consistency and efficacy. It was expected to form an assistive tool in TMH quantitation and subsequently and screening on Dry Eyes Disease. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.bspc.2021.102655 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL |
Keywords | DocType | Volume |
Tear meniscus height, Dry eyes, Deep learning, Auto quantification | Journal | 68 |
ISSN | Citations | PageRank |
1746-8094 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianyu Deng | 1 | 0 | 0.34 |
| Lei Tian | 2 | 24 | 10.34 |
| Ziyu Liu | 3 | 0 | 0.34 |
| Yongjin Zhou | 4 | 0 | 1.35 |
| Ying Jie | 5 | 0 | 0.34 |