Deep learning segmentation for optical coherence tomography measurements of the lower tear meniscus

Abstract

The tear meniscus contains most of the tear fluid and therefore is a good indicator for the state of the tear film. Previously, we used a custom-built optical coherence tomography (OCT) system to study the lower tear meniscus by automatically segmenting the image data with a thresholding-based segmentation algorithm (TBSA). In this report, we investigate whether the results of this image segmentation algorithm are suitable to train a neural network in order to obtain similar or better segmentation results with shorter processing times. Considering the class imbalance problem, we compare two approaches, one directly segmenting the tear meniscus (DSA), the other first localizing the region of interest and then segmenting within the higher resolution image section (LSA). A total of 6658 images labeled by the TBSA were used to train deep convolutional neural networks with supervised learning. Five-fold cross-validation reveals a sensitivity of 96.36% and 96.43%, a specificity of 99.98% and 99.86% and a Jaccard index of 93.24% and 93.16% for the DSA and LSA, respectively. Average segmentation times are up to 228 times faster than the TBSA. Additionally, we report the behavior of the DSA and LSA in cases challenging for the TBSA and further test the applicability to measurements acquired with a commercially available OCT system. The application of deep learning for the segmentation of the tear meniscus provides a powerful tool for the assessment of the tear film, supporting studies for the investigation of the pathophysiology of dry eye-related diseases.

Artificial Intelligence in Dry Eye Disease

“…For image segmentation, the support for a segmented area can be calculated as the number of pixels in the segmented area divided by the number of background pixels [40].…”

“…The two studies [78,40] showed that CNNs could be an appropriate tool for image analysis. CNNs are likely to increase in popularity within the field of DED due to promising results for solving image related tasks, including feature extraction.…”

“…Among the reviewed studies, some choose not to use the model predictions of DED when the predicted probabilities are too close to 0.5, reflecting that the model is uncertain [73]. Others report the standard deviation of the model performance scores [12,34,35,62,32,33,40,47,61]. Some computes the confidence intervals for the model performance scores [30,37,72,61,61].…”

“…This is one out of two studies in this review that used an independent test sets after model development. Such practice is essential for a realistic impression of how well the model OCT images from healthy subjects for automatic detection of the lower tear meniscus [40]. Two different CNNs were trained and evaluated using 5-fold cross validation.…”

Advances in Imaging Technology of Anterior Segment of the Eye

“…In dry eye disease, deep learning can be applied for the automatic segmentation of the anterior segment OCT image with a thresholding-based segmentation algorithm for the evaluation of the tear meniscus [ 91 ].…”