An edge detection-based deep learning approach for tear meniscus height measurement

arXiv:2403.15853v1 Announce Type: cross
Abstract: 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 …

abstract annotation arxiv cs.cv deep learning deep learning techniques detection edge eess.iv however labor measurement paper type