Nellie: Automated organelle segmentation, tracking, and hierarchical
  feature extraction in 2D/3D live-cell microscopy
Austin E. Y. T. Lefebvre Gabriel Sturm Ting-Yu Lin Emily Stoops Magdalena Preciado Lopez Benjamin Kaufmann-Malaga Kayley Hake
The analysis of dynamic organelles remains a formidable challenge, though key
to understanding biological processes. We introduce Nellie, an automated and
unbiased pipeline for segmentation, tracking, and feature extraction of diverse
intracellular structures. Nellie adapts to image metadata, eliminating user
input. Nellie's preprocessing pipeline enhances structural contrast on multiple
intracellular scales allowing for robust hierarchical segmentation of
sub-organellar regions. Internal motion capture markers are generated and
tracked via a radius-adaptive pattern matching scheme, and used as guides for
sub-voxel flow interpolation. Nellie extracts a plethora of features at
multiple hierarchical levels for deep and customizable analysis. Nellie
features a Napari-based GUI that allows for code-free operation and
visualization, while its modular open-source codebase invites customization by
experienced users. We demonstrate Nellie's wide variety of use cases with two
examples: unmixing multiple organelles from a single channel using
feature-based classification and training an unsupervised graph autoencoder on
mitochondrial multi-mesh graphs to quantify latent space embedding changes
following ionomycin treatment.