Author	Project	Build Status	License	Code Quality	Coverage
G. Filitto	MRI colorectal cancer segmentation	Linux :		Codacy : Codebeat :

img-segm

This package allows to segment cancer regions on T2-weighted Magnetic Resonance Images (MRI) of patients affected by colorectal cancer. The segmentation approach is based on Convolutional Neural Networks (CNNs) like U-Net. This package provides a series of modules and scripts to visualize, pre-process the DICOM files and to train a U-Net model.

1. Overview
2. Contents
3. Prerequisites
4. Installation
5. Usage
6. License
7. Author
8. Citation

Overview

Colorectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of one of the cells making up the colorectal tract. Colorectal cancer is the second malignant tumor per number of deaths after the lung cancer and the third per number of new cases after the breast and lung cancer. Risk factors for this kind of cancer include colon polyps, long-standing ulcerative colitis, diabetes II and genetic history (HNPCC or Lynch syndrome). In order to get information about diagnosis, therapy evaluation on colorectal cancer, analysis on radiological images can be performed through the application of dedicated algorithms.

In this scenario, the correct and fast identification of the cancer regions is a fundamental task. Up to now this task is performed using manual or semi-automatic techniques, which are time-consuming and subjected to the operator expertise.

This project provides an automatic pipeline for the segmentation of cancer areas on T2-weighted Magnetic Resonance Images of patient affected by colorectal cancer. The segmentation is achieved with a Convolutional Neural Network like U-Net.

Example of segmentation . Left): Original MRI scan of a patient affected by colorectal cancer. Right): Original MRI scan of a patient affected by colorectal cancer with identified tumor area.

Contents

img-segm is composed of a series of modules contained in MRIsegm and scripts in utils:

• modules allow one to load, visualize, processing the DICOM series and to train a U-Net model.
• scripts provide a fast way to handle DICOM series and ROI from command line.

The documentation can be found here.

For a better description of each module:

Module	Description
utils	methods to load and visualize the DICOM series and the relative Region Of Interest (ROI)
processing	methods to perform operations such as denoising, resizing, predict the images
models	contains the implementation of a U-net model
datagenerators	contains methods to generate data for training the model
metrics	contains useful metrics for training the model
losses	contains useful losses for training the model
graphics	methods to display the predictions

notes:

• It is also possible to use models, metrics and losses from the segmentation-models library.

For a better description of each script:

Script	Description
dcmsorter	move and sort DICOM files from nested dirs like patientID/EXAMINATION/DIRECTORY1/DIRECTORY2 to patientID/EXAMINATION
dcm2img	convert DICOM series to .png images
roimanager	show the ROI saved as .roi over the images

For the usage of each script please refer to the documentation.

Pre-requisites

Supported python:

First of all ensure to have the right python version installed.

This project use Tensorflow, opencv-python, numpy: see the requirements for more information.

⚠️ Tensorflow v2.5 requires the minimum Python version to be 3.6!

Installation

First, clone the git repository and change directory:

git clone https://github.com/giuseppefilitto/img-segm.git
cd img-segm

Then, pip-install the requirements and run the setup script:

pip install -r requirements.txt

⚠️ Apple Silicon: ensure to have installed a proper Conda env, then install tensorflow as explained here. Finally, install one by one the remaining dependencies using conda (if available) or pip.

python setup.py install

Testing

Testing routines use PyTest and Hypothesis packages. A full set of test is provided in testing directory. You can run the full list of test with:

python -m pytest

Usage

Once you have installed it, you can start to segment the images directly from your bash. The input --dir is the path of the dir containing the DICOM series. Please ensure that the folder contains only one series. If the directory is a nested dir, the script will find automatically the sub-dir containing the DICOM series.

Quick Start

   python -m MRIsegm --dir='/path/to/input/series/'  

where:

• --dir is the path of the directory containing the DICOM series (required).

Options

--model

Name of the model's weights saved in the weights dir.

  python -m MRIsegm --dir='/path/to/input/series/' --model='model_name'

notes:

• model_name set as default:efficientnetb0_BTC=4_full_150E_OPT=adam_LOSS=dice_loss_plus_1binary_focal_loss
• Remember to specify the name without_weights.h5
• you can also use your own model's weight saving the weights in the weights dir as model_name_weights.h5.

⚠️ You need to save also the architecture as model_name.json file in the same dir.

--mask

When enabled plot the predicted binary [0, 1] mask of each slice.

   python -m MRIsegm --dir='/path/to/input/series/'  --mask

--density

When enabled plot the predicted probability map between 0. and 1. of each slice over the original image.

   python -m MRIsegm --dir='/path/to/input/series/'  --density

--3D

When enabled plot the a 3D mesh of the segmented areas.

   python -m MRIsegm --dir='/path/to/input/series/'  --3D

Examples

Several examples are available under the notebooks dir.

Segmentation:

Purpose	Example
load, visualize DICOM series	DicomExplorer
perform Image processing operations	ImageProcessing
Prepare the data for model training	PrepareData
train a U-net model	UNet-Segmentation
train a model from segmentation-models	Segmentation
Display the predictions	VisualizePredictions
Save the predicted image	SavePredictions
Convert model to model's weights	ConverterToWeights
plot 3D mesh	3D-mesh

features analysis:

Purpose	Example
Feature Extraction	FeaturesExtraction
Features Analysis	Features Analysis
Write .dcm files	Writedicom
Write .nrrd files	sitkvolumeR2

License

This package is licensed under the MIT "Expat" License.

Author

• Giuseppe Filitto git

Citation

If you have found img-segm helpful in your research, please consider citing this project

@misc{MRI colorectal cancer segmentation,
  author = {Filitto, Giuseppe},
  title = {MRI colorectal cancer segmentation},
  year = {2021},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/giuseppefilitto/img-segm}},
}