darule0/exophytic-cyst-segmentation

ExoCystSegNet

This software package (ExoCystSegNet) includes the source codes for a fully automated segmentation of kidneys and exophytic cysts in patients with autosomal dominant polycystic kidney disease (ADPKD). The nnU-Net was utilized to train the neural networks, and this package is for testing the pretrained neural networks to automatically segment right kidney, left kidney, and exophytic cysts. You can refer to the following paper if you would like to know what the exophytic cysts are:

Bae, K. T., Shi, T., Tao, C., et al. (2020). Expanded Imaging Classification of 
Autosomal Dominant Polycystic Kidney Disease. Journal of the American Society of 
Nephrology, 31(7), 1640-1651.

ExoCystSegNet was trained and tested using T₂-MR images. The paper illustrating training procedure and details of training and testing datasets was submitted to the journal, and it will be updated once the paper is published.

Installation

Prior to using ExoCystSegNet, you need to install PyTorch and nnU-Net. Refer to the corresponding website and follow the instructions to install PyTorch and nnU-Net.

Note that ExoCystSegNet was tested on Linux (Ubuntu 16.04 LTS), and we do not guarantee if ExoCystSegNet works on other operating systems (Windows or macOS). We recommend to use virtual environment (e.g., Anaconda).

Usage

ExoCystSegNet consists of 2 main modules: 1) automated segmentation of kidneys and exophytic cysts generating region mask files (NIfTI format) and 2) Plotting label overlayed images on MR images.

1) Segmentation and generation of region mask files

This module uses pretrained weights stored in pretrained_model and loads test images in the images_test folder. In the images_test folder, there are 3 cases with exophytic cysts and 3 cases without exophytic cysts.

auto_eexo_seg_main.py -i [input_folder] -o [output_folder]

The input_folder and output_folder are set to images_test and eexo_seg_results_raw as a default, respectively. Specifying input and output folders is optional, so you can just put auto_eexo_seg_main.py if you want to test the default files.

2) Plotting label overlayed images

Plotting mask overlayed images module loads the original MR images and mask files and generate multiple images (slice-by-slice) overlayed in different colors with the corresponding kidneys and exophytic cysts masks (blue: right kidney, red: left kidney, green: exophytic cyst). The images are saved in eexo_seg_mask_overlay folder.

plot_maskoverlay_images_main.py -i [input_folder] -l [label_folder] -o [output_folder]

Specifying input, label and output folders is also optional, so you can just put plot_maskoverlay_images_main.py if you want to plot the images with the default files. The examples of saved images are illustrated below:

(Example 1) Label overlayed images (case without exophytic cysts)

(Example 2) Label overlayed images (case with exophytic cysts)