Welcome to Image Classifier 3D’s documentation!

Image Classifier 3D

Build Status Documentation

Python package for building 3d image classifier using deep neural networks

This was used to build the cell classifier to automatically annotate the cells (as interphase, prophase, metaphase, anaphase/telophase or outliers) in hIPSC single cell dataset from Allen Institute for Cell Science.

Installation

Stable Release: pip install image_classifier_3d

Install nightly development head and make customization:

git clone git@github.com:AllenCell/image_classifier_3d.git
cd image_classifier_3d
pip install -e .[all]

Documentation

Quick Start:

  1. Training

Set up a yaml file to specify training configurations. See an example HERE with embedded instructions.

run_classifier3d --debug train --config /path/to/config_train.yaml

How to prepare your training data? All data should be saved in a folder with filenames in the format X_CELLID.npy, where X can be any integer from 0 to num_class-1 (assuming num_class <= 10), and CELLID is a unique name for the cell (e.g., using uuid). This .npy file can be generated from a cropped image of this cell with dna channel and cell membrane channel, together with cell segmentation of this cell. See details in this script

  1. Testing (with known labels, reporting performance)

Set up a yaml file to specify testing configurations. See an example HERE.

run_classifier3d --debug evaluate --config /path/to/config_evaluate.yaml --outout_path /path/to/output

  1. Inference (without labels, annotating new cells)

It is possible to run inference as training/testing, just with inference. You may pass in a config file, like HERE. If no config file is passed in, a default one will be loaded.

run_classifier3d --debug inference --csv /path/to/csv --config /path/to/config_infernece.yaml --outout_path /path/to/output

However, it might be more common that you want to call the classifier in other python scripts, for example, as one step of you bigger workflow.

my_classifier = ProjectTester(mode="inference", save_model_output=False)
df_pred = my_classifier.run_tester_csv(dataset, pred_path, return_df=True)

Here, dataset is a filename pointing to a csv file listing all the images to be applied on. See ``test_csv` mode in dataloader <https://allencell.github.io/image_classifier_3d/image_classifier_3d.data_loader.html#image_classifier_3d.data_loader.universal_loader.adaptive_padding_loader>`_ for details. pred_path is a filepath to save intermediate prediction tables (e.g., predictions from each individual model when using ensemble). The final labels are returned to a dataframe df_pred.

Full Documentation:

For full package documentation please visit AllenCell.github.io/image_classifier_3d.

Development

See CONTRIBUTING.md for information related to developing the code.

Questions?

If you have any questions, feel free to leave a comment in our Allen Cell forum: https://forum.allencell.org/.

**Free software: Allen Institute Software License**

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