An edge detection-based deep learning approach for tear meniscus height
  measurement
Kesheng Wang Kunhui Xu Xiaoyu Chen Chunlei He Jianfeng Zhang Dexing Kong Qi Dai Shoujun Huang
Automatic measurements of tear meniscus height (TMH) have been achieved by
using deep learning techniques; however, annotation is significantly influenced
by subjective factors and is both time-consuming and labor-intensive. In this
paper, we introduce an automatic TMH measurement technique based on edge
detection-assisted annotation within a deep learning framework. This method
generates mask labels less affected by subjective factors with enhanced
efficiency compared to previous annotation approaches. For improved
segmentation of the pupil and tear meniscus areas, the convolutional neural
network Inceptionv3 was first implemented as an image quality assessment model,
effectively identifying higher-quality images with an accuracy of 98.224%.
Subsequently, by using the generated labels, various algorithms, including
Unet, ResUnet, Deeplabv3+FcnResnet101, Deeplabv3+FcnResnet50, FcnResnet50, and
FcnResnet101 were trained, with Unet demonstrating the best performance.
Finally, Unet was used for automatic pupil and tear meniscus segmentation to
locate the center of the pupil and calculate TMH,respectively. An evaluation of
the mask quality predicted by Unet indicated a Mean Intersection over Union of
0.9362, a recall of 0.9261, a precision of 0.9423, and an F1-Score of 0.9326.
Additionally, the TMH predicted by the model was assessed, with the fitting
curve represented as y= 0.982x-0.862, an overall correlation coefficient of
r^2=0.961 , and an accuracy of 94.80% (237/250). In summary, the algorithm can
automatically screen images based on their quality,segment the pupil and tear
meniscus areas, and automatically measure TMH. Measurement results using the AI
algorithm demonstrate a high level of consistency with manual measurements,
offering significant support to clinical doctors in diagnosing dry eye disease.