Conference site ยป Proceedings

Low Level Feature Extraction for Cilia Segmentation

Meekail Zain
Department of Computer Science, University of Georgia, Athens, GA 30602 USA

Eric Miller
Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602 USA

Shannon P Quinn
Department of Computer Science, University of Georgia, Athens, GA 30602 USA
Department of Cellular Biology, University of Georgia, Athens, GA 30602 USA

Cecilia Lo
Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA 15261 USA


Cilia are organelles found on the surface of some cells in the human body that sweep rhythmically to transport substances. Dysfunction of ciliary motion is often indicative of diseases known as ciliopathies, which disrupt the functionality of macroscopic structures within the lungs, kidneys and other organs li2018composite. Phenotyping ciliary motion is an essential step towards understanding ciliopathies; however, this is generally an expert-intensive process quinn2015automated. A means of automatically parsing recordings of cilia to determine useful information would greatly reduce the amount of expert intervention required. This would not only improve overall throughput, but also mitigate human error, and greatly improve the accessibility of cilia-based insights. Such automation is difficult to achieve due to the noisy, partially occluded and potentially out-of-phase imagery used to represent cilia, as well as the fact that cilia occupy a minority of any given image. Segmentation of cilia mitigates these issues, and is thus a critical step in enabling a powerful pipeline. However, cilia are notoriously difficult to properly segment in most imagery, imposing a bottleneck on the pipeline. Experimentation on and evaluation of alternative methods for feature extraction of cilia imagery hence provide the building blocks of a more potent segmentation model. Current experiments show up to a 10\% improvement over base segmentation models using a novel combination of feature extractors.


cilia, segmentation, u-net, deep learning



Bibtex entry

Full text PDF