Deep learning for liver NAS and fibrosis scoring

Repository for the publication "Deep learning enables pathologist-like scoring of NASH models". Nature scientific reports, 2019: https://www.nature.com/articles/s41598-019-54904-6.

Input:

• Tiles of microscopy images of of mouse or rat liver tissue, stained with Masson's trichrome (see below for details on tiles).

Output / result:

• Discrete pathologist-like liver scores on ballooning, inflammation, steatosis (NAS-score) and fibrosis with scores optimized to follow the scores of a given ground truth with minimal error (e.g. a pathologist).
• Continuous liver scores on ballooning, inflammation, steatosis (NAS-score) and fibrosis.
• Spatial feature distribution of ballooning, inflammation, steatosis (NAS-score) and fibrosis.

Requirements:

• Python 3.x
• Libraries: keras, tensorflow, matplotlib, pandas, sklearn, yaml (plus a number of common libraries, you will find them as imports)

Set-up:

1. Clone this repository
2. Download images, model weights and other files from: https://osf.io/p48rd/ and extract all data under ./model/. Some subfolders contain .zip files, which need to be extracted by hand at their respective locations.

Training a new model:

Complete train.yaml (or a copy):

A least you need to set:

• model_path (Path where the .h5 file with the CNN weights will be stored)
• model_file_name (Filename of model)
• ground_truth_path (Path where the tiles with the ground truth are located)

Start training:

$python train.py -c train.yaml

This will generate a new model file.

Please note, that trained models for ballooning, inflammation, steatosis and fibrosis have been uploaded to: https://osf.io/p48rd/ and there is no need to train a new model unless you want to add own data.

Classification of a scanned liver section:

Prerequisite

• Higher resolution tiles of Masson trichrome stained liver (0.44 µm/px, 299x299 px², for ballooning, inflammation and steatosis) (Placed under: ./classification_data/<exp_no>/tiles/tiles/)

• Lower resolution tiles of Masson trichrome stained liver (1.32 µm/px, 299x299 px², for fibrosis) (Placed under: ./classification_data/<exp_no>/big_tiles/tiles/)

• Completed settings file: classify_Kleiner_score.yaml (or a copy).

Run:

$python classify_Kleiner_score.py -c classify_Kleiner_score.yaml

This will create the following files

• <exp_no>_summary.csv
• <exp_no>_Ballooning_sub_score.csv
• <exp_no>_Inflammation_sub_score.csv
• <exp_no>_Steatosis_sub_score.csv
• <exp_no>_Fibrosis_score.csv

The first file contains results summarized per liver including discrete pathologist-like scores and continuous scores. The last four contain spatial data for each result (full results).

Alternative (optional):

(Classify each score individually:)

Prerequisite

• Higher resolution tiles of Masson trichrome stained liver (0.44 µm/px, 299x299 px², for ballooning, inflammation and steatosis) (Placed under: ./classification_data/<exp_no>/tiles/tiles/)

• Lower resolution tiles of Masson trichrome stained liver (1.32 µm/px, 299x299 px², for fibrosis) (Placed under: ./classification_data/<exp_no>/big_tiles/tiles/)

• Completed settings file: classify.yaml (or a copy).

Run:

$python classify.py -c classify.yaml

Two files will be created:

• <exp_no_score_name>_summary.csv
• <exp_no_score_name>.csv

The first file contains results summarized per liver including discrete pathologist-like scores and continuous scores. The second file contains spatial data for the score specified in classify.yaml (full results).

Determination of new thresholds, and / or computation of evaluation parameters:

Complete fit_threshold_settings.yaml

Run:

$python fit_thresholds.py -c fit_threshold_settings.yaml

Output:

• Thresholds to map continuous liver scores to discrete pathologist scores with minimized error (if fit_new_thresholds = True).
• Output of various evaluation parameters (mean absolute error, weighted precision, weighted F1, Cohens Kappa) comparing ground truth of NAS and fibrosis score with computeted result of NAS and fibrosis score

Class activation maps:

Run within jupyter notebook and complete paths inside:

CNN_class_activation_map.ipynb