Stacked Neural Networks for end-to-end ciliary motion analysis
This provides a fully-automated analysis toolbox for ciliopathy diagnosis, addressing scalability issues in medical imaging, though it appears incremental compared to existing computational pipelines.
The researchers tackled the problem of laborious and error-prone manual ciliary motion analysis by developing an end-to-end machine learning pipeline that automatically identifies cilia regions from videos and classifies patients as normal or abnormal, achieving 90% accuracy with only a few hundred training epochs.
Cilia are hairlike structures protruding from nearly every cell in the body. Diseases known as ciliopathies, where cilia function is disrupted, can result in a wide spectrum of disorders. However, most techniques for assessing ciliary motion rely on manual identification and tracking of cilia; this process is laborious and error-prone, and does not scale well. Even where automated ciliary motion analysis tools exist, their applicability is limited. Here, we propose an end-to-end computational machine learning pipeline that automatically identifies regions of cilia from videos, extracts patches of cilia, and classifies patients as exhibiting normal or abnormal ciliary motion. In particular, we demonstrate how convolutional LSTM are able to encode complex features while remaining sensitive enough to differentiate between a variety of motion patterns. Our framework achieves 90% with only a few hundred training epochs. We find that the combination of segmentation and classification networks in a single pipeline yields performance comparable to existing computational pipelines, while providing the additional benefit of an end-to-end, fully-automated analysis toolbox for ciliary motion.