infer-subc v1.0.0
We are happy to be sharing infer-subc v.1.0.0 with the community. 🎉
About
infer-subc is a Python-base bioimage analysis package used to perform instance segmentation and quantification on confocal microscopy images containing fluorescently labeled organelles.
Features
Segmentation Workflows
infer-subc Segmentation Workflows are available through the co-developed organelle-segmenter-plugin for Napari. Instructions for use are found within this package. The following features are included:
Instance segmentation of:
- Lysosomes (lyso)
- Mitochondria (mito)
- Golgi
- Peroxisomes (perox)
- Endoplasmic reticulum (ER)
- Lipid droplets (LD)
- Cell mask
- Nuclei mask
Organelle quantification includes:
- Morphology metrics (based on skimage.measure.regionprops)
- Pairwise interaction between organelles
- Subcellular distribution metrics (leveraging frameworks included in the CellProfiler MeasureObjectIntensityDistribution module)
Additional Notes
Contributions & Comments
With several of us being new to the Python development work, we genuinely appreciate and openly accept any comments to improve our package. Please make use of the Issues and Discussion features in GitHub or reach out by email.
Napari Plugin Framework
The framework for our implementation of infer-subc in Napari via the co-developed organelle-segmenter-plugin is based on the Allen Cell & Structure Segmenter Napari plugin. We particularly leverage their workflow paradigm which is integral in the use of the Napari plugin interface. Although the logic of our multi-channel organelle segmentations required us to fork and modify their code, we hope it provides a stable, but evolving base which will help manage accumulation of technical debt.
Contributions
The primary developers of this release are @shanrhoads and @ergonyc. Contributions were also made by @mwanacongo.
Support
Support for this project includes the CZI Neurodegeneration Challenge Network (NDCN) and Nation Institute of Health under awards T32NS007431, F31AG079622, R01NS105981, and R35GM133460.
