• Introduction
• Requirements
• Run
• Publications

This repository contains the work during the PhD of Carlos Tor-Díez, titled "Automatic segmentation of the cortical surface in neonatal brain MRI". It includes two main contributions:

• patchBasedSegmentation.py, where several methods of label fusion (the final step of the multi-atlas segmentation approaches) are performed, including IMAPA [1].
• topologicalCorrection.py, where a topological correction for segmentation is presented, which consists in a multi-scale, multi-label homotopic deformation [2].

All scripts were coded in python 2.7, but we are working to be compatible to python 3.7.

The file called requirements.txt helps to install all the python libraries.

• Using pip:

pip install -r requirements.txt

• Using anaconda:

conda install --file requirements.txt

Example of IMAPA application using a atlas set of two pairs of images using two iterations (alpha = 0 and alpha = 0.25) using 4 threads in parallel:

python neoSeg/patchBasedSegmentation.py  -i  brain.nii.gz -a  atlas1_registered_HM.nii.gz  atlas2_registered_HM.nii.gz -l  label1_propagated.nii.gz  label2_propagated.nii.gz  -mask mask.nii.gz  -m IMAPA  -hss 3  -hps 1  -k 15  -alphas 0 0.25  -t 4

i: input anatomical image

a: anatomical atlas images in the input space

l: label atlas images in the input space

mask: binary image for input

m: segmentation method chosen (LP, S_opt, I_opt, IS_opt or IMAPA)

hss: half search window size

hps: half patch size

k: k-Nearest Neighbors (kNN)

alphas: alphas parameter for IS_opt and IMAPA methods

t: Number of threads (0 for the maximum number of cores available)

Note: We recommend to previously register the intensity image from the atlas set to the input image, apply a histogram matching algorithm and propagate the transformations to the label maps.

Coming soon...

• [1] C. Tor-Díez, N. Passat, I. Bloch, S. Faisan, N. Bednarek and F. Rousseau, “An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI,” Computerized Medical Imaging and Graphics, 70:73–82, 2018, hal-01761063.

• [2] C. Tor-Díez, S. Faisan, L. Mazo, N. Bednarek, Hélène Meunier, I. Bloch, N. Passat and F. Rousseau, “Multilabel, multiscale topological transformation for cerebral MRI segmentation post-processing,” In 14th International Symposium on Mathematical Morphology (ISMM 2019), pp. 471–482, 2019, hal-01982972.