overview

infer_subc

This module contains code to segment organelles from multi-channel images generated by SCohenLab. The "raw" data files are the output of linear unmixing of multi-spectral imaging capture in their lab.

NOTE: this is designed to work with a second repo organelle-segmenter-plugin which instantiates a plugin for napari.

In addition to the python based module there are a series of expository Jupyter notebooks which demonstrate the logic and development of the library.

organelles

These are function to infer each specific organelles from their respective channels: Nuclei, Cellmask (Cell Membrane TBD), Lysosome, Mitochondria, Golgi, Peroxisome, Endoplasmic Reticulum, and Lipid bodies.

core

This submodule contains functions for handling the file systems and input / output, as well as the core image processing. The bulk of the image processing functions are simple wrappers to scipy and numpy image processing functions as well as functions from the Allen Cell Segmentation (aicssegmentaion) library. utils.img contains most of the specific image processing routines employed in segmentation, while utils.file_io handles loading and saving the data files.

utils

This submodule contains functions for handling the file systems and input / output, as well as the core image processing. The bulk of the image processing functions are simple wrappers to scipy and numpy image processing functions as well as functions from the Allen Cell Segmentation (aicssegmentaion) library. utils.img contains most of the specific image processing routines employed in segmentation, while utils.file_io handles loading and saving the data files.

workflows

This submodule (hard forked from aicssegmentation) works with the napari plugin to provide interactive GUI control of the segmentaitons.

etc

batch

This submodule contains functions to process each multi-channel/spectral image to infer ALL organelles

constants, exceptions