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The dynamic variation of the human tear meniscus (tears around the eye lids) is very critical in visual function, maintenance of corneal integrity, and ocular comfort. The quantitative measuring of the tear menisci around the eyelids is though a challenging task. In our work, tear meniscus images are obtained with our custom-built optical coherence tomography (OCT) and are processed using our novel segmentation method. For the latter, we use an implicit deformable model driven by a conditional random field (CRF). The evolution of the model is solved as MAP estimation. The target conditional probability is decomposed using a simple graphical model, where the probability field of the pixel labels given the image observations is estimated using a discriminative CRF. Our results show that our segmentation approach successfully handles clutter and boundary ambiguities of the tear menisci, which makes our integrated system reliable for the every day medical practice.