MMA-MISP 1.0.0

A test package

Medical Image Subregion Partiton Toolkit (MISP)

Linux	Windows	Mac OS
Passed	Passed	Passed

Welcome to Medical Image Subregion Partiton Toolkit (MISP) package.

MISP is a Python package, which is able to detect and segment subregions within tumor on medical images, such as CT and MRI. Note that, MISP is not only developed for 3D data, but also for 2D data (e.g., single slice).

MISP is designed and developed by Zhenwei Shi, Zhihe Zhao and other AI and CS scientists from Media Lab. Also, the work is supported by well experienced radiologists MD Zaiyi Liu and Changhong Liang from Guangdong Provincial People's Hospital.

The workflow of MISP includes four major stages:

• Data pre-processing
• Subregion pre-segmentation
• Quantitative imaging feature extraction
• Clustering in population-level

Installation

pip install MMA_MISP

Features

• Medical image data pre-processing, including data load, crop, normalization and so on.
• Automatic generation of multiple regions of interest surrounding tumor, such as peri-tumor and tumor ring
• Subregion pre-segmentation by image properties
• Quantitative imaging feature (e.g., Radiomics) extraction
• Unsupervised clustering algorithms for untimate medical image subregion partition
• Visualiation

Tutorial

Step 1: Package loading

import numpy as np
import matplotlib.pyplot as plt
import MISP
from MISP.function import makedirs, extract_main, feature_extract_main, cluster_main, cluster_main_predict

Step 2: Parameter setting

You can download the demo dataset here and the initial .yaml file for pyradiomics here MISP.

dataset_path = 'dataset'
image_path = dataset_path+'/image'
mask_path = dataset_path+'/mask'
out_path = 'subregion_SLIC_output/SuperVoxel'
yaml_path = 'radiomics_features.yaml'
sv_path = os.path.join(out_path,'supervoxel')
csv_path = os.path.join(out_path,'csv')
concat_path = os.path.join(out_path, 'concat_mask')

Make paths.

makedirs(sv_path)
makedirs(csv_path)
makedirs(concat_path)

You can change the size of the peritumor or tumor ring area with parameter kernel, and select the subregion mode: 'initial', 'peritumor', 'tumor_ring'.

mode = 'peritumor' # 'initial','peritumor','tumor_ring'
kernel = 3

Step 3: Pre-segmentation of subregions

extract_main(image_path, mask_path, sv_path, out_path, mode, kernel)

Step 4: Quantitative imaging feature extraction

feature_extract_main(sv_path, csv_path,yaml_path)

Step 5: Generation of subregion partition map

cluster_main(image_path, csv_path, sv_path, concat_path, out_path)

Step 6: Visualization of subregion partition map

License

MISP may not be used for commercial purposes. This package is freely available to browse, download, and use for scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License.

Main Developers

• Zhenwei Shi ^1,^ ^2^
• [Zhihe Zhao] ^1,^ ^2^
• [Chu Han] ^1,^ ^2^
• [Changhong Liang] ^1,^ ^2^
• [Zaiyi Liu] ^1,^ ^2^

^1^ Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences),Southern Medical University ^2^ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, China

Contact

We are happy to help you with any questions. Please contact Zhenwei Shi. Email: [e-mail]

We welcome contributions to MISP.