LuNG3D

Synthetic Lung Nodule 3D Image Generation Using Autoencoders

This repository contains the autoencoder and lung nodule analyzer used in "Synthetic Lung Nodule 3D Image Generation Using Autoencoders"(https://arxiv.org/abs/1811.07999).

This code is expected to be usable with Python 3.6.5 and PyTorch 0.4.1, but this code is for example reference only and is not maintained.

The majority of the analyzer code in myALNSB is cloned from "ALNSB: the Adaptive Lung Nodule Screening Benchmark"(https://github.com/sincewhenUCLA/Lung-Nodule-Detection-C-Benchmark).

LuNG

The python scripts below are in LuNG:

• Shapes.py Primary Python code which loads in 3D training images, defines autoencoder, trains model, and creates generated images.
• nofeed6.py Runs 6 training iteration blocks allowing interaction with ALNSB for evaluation. Does not use ALNSB-approved nodules for training augmentation.
• feedtwice.py Runs 6 training iteration blocks allowing interaction with ALNSB for evaluation and training augmentation. Augments 2 of the 6 iteration blocks.

myALNSB

The scripts below are in myALNSB/Lung-Nodule-Detection-C-Benchmark:

• run4Single.sh
  • Run 4 model directories in parallel.
  • Waits for LuNG Python generation scripts to produce rnd.csv file.
  • Creates segInputOverride.csv which is used by alnsb for nodule processing Normally, alnsb processes the raw image which is in images/NLST_R0960B_OUT4
  • Creates img2.csv (possible training augmentation) and feat.csv (analyzer evaluation of nodules).
• run64Single.sh runs 6 iteration of run4Single to interact with nofeed6.py or feedtwice.py autoencoder training and generation.

In myALNSB/Lung-Nodule-Dection-C-Benchmark/stages/classification:

• classification_step.c: processes nodules for classifier step and generates accepted images with KEEPCSV tokens and feature data with KEEPFEAT tokens.

Author

Steve Kommrusch is the author of the LuNG autoencoder code, changes to the ALNSB analyzer, summary, and processing scripts.

LuNG3D