Optical Coherence Tomography (OCT) has established itself as an important tool for studying the role of cilia in Mucociliary Clearance (MCC) due to its ability to observe the cilia’s temporal characteristics over a large field of view. To obtain useful, quantitative measures of this dynamic morphology, the ciliated layer of tissue needs to be segmented from other static components. This is currently accomplished using Speckle Variance processing, a technique whose success relies on subjective thresholding and lacks sensitivity to other sources of speckle noise. We present a modified, frequency constrained, version of Robust Principle Component Analysis (RPCA) which we call Frequency Constrained RPCA (FC-RPCA) as an alternative method for dynamic segmentation of cilia from time-varying OCT B-scans. Based in Sparse Representation theory, FC-RPCA decomposes stacks of images in time into low-rank (static) and a sparse (dynamic) matrices. The sparse matrix represents the segmented cilia layer because of the sparse frequency spectrum exhibited by their characteristic beating pattern. This novel algorithm introduces an additional feature, a user defined frequency constraint on the sparse component, which prevents other sources of speckle noise, like slow moving mucus clouds at the tissues surface, from being segmented with the cilia. The algorithm was used to segment motile cilia in 17 datasets of ex-vivo human ciliated epithelium with high accuracy. Furthermore, FC-RPCA requires no parameter tuning across datasets, demonstrating its capability as a robust tool for processing large volumes of data. When compared with the standard Speckle Variance method, FC-RPCA performed with improved accuracy and selectivity.