Playing with SimpleITK and nnU-Net

This repository contains Python code to play with SimpleITK and nnU-Net.

Requirements

• Install Python 3.X,
• Install the required packages:

pip install -r requirements.txt

Data

• Download data from the Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) challenge,
• Extract the archive to data/.

Alternatively, data can be found in Releases on Github.

Usage

Prepare data

• Convert data from DICOM to NifTI with SimpleITK:

python convert_to_nii.py

• Follow instructions for the folder structure of data, as detailed for nnU-Net.

Train nnU-Net

• Run the playing_with_pytorch.ipynb notebook.

Training was performed with:

• a training dataset of 5 images downsampled to 4x4x4mm,
• no cross-validation (in which case the validation dataset is the same as the training dataset to find the best checkpoint and to decrease the learning rate).

Training for 1 epoch can require 2m30s, but up to 10m, depending on the machine which you obtained.

Results obtained after ~ 100 epochs are shown below:

where:

• training loss is in blue,
• validation loss is in red,
• evaluation metric (to maximize) is in green.

In terms of Dice scores, the average is:

• 99.1% for the 5 patients of the training data,
• 94.8% for the 15 patients of the validation data (training data not used).

References

• Github repository for SimpleITK,
• Notebooks for SimpleITK,
• Documentation for SimpleITK,
• nnU-Net,
• Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) challenge,
• Evaluation of segmentation results with tools provided by organizers of CHAOS.