To get a local copy up and running follow these simple steps.
The requirements.txt must be installed if you run on a local system; otherwise, installing libraries is not required if you run on Google Collab.
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Clone the project
git clone https://github.com/asharma8602/Brain-Image-Segmentation
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Before running the Python Notebook on your local system.
pip install -r requirements.txt
├───Train_Images
└───Train_Labels
├───Contours
├───Distance_Maps
└───Masks
Indian Brain Segmentation Dataset (IBSD) consists of high-quality 1.5T T1w MRI data of 114 subjects generated under fixed imaging protocol along with corresponding manual annotation data of 14 sub-cortical structures done by expert radiologists. The number of MR scans in the dataset consists of an approximately equal number of male and female subjects belonging to a young age group (20-30 years). This data has been used to create a template for the young Indian population.
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Reading MRI data of 113 subjects in the form of NiFti Images.
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Combining 14 labels to establish a binary classification Psi-net.
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Saving 192 slices per subject to adjust the data corresponding to Psi-net architecture.
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Conversion of labels to Binary Masks which further being converted to Contours and finally obtaining Distance Maps for them.
A custom data-loader function is designed with following functions.
- loadimage - Loading input image
- loadmask - Loading mask of corresponding label
- loadcont - Loading contour of corresponding label
- loaddist - Loading distance map of corresponding label
@article{Murugesan2019PsiNetSA,
title={Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation},
author={Balamurali Murugesan and Kaushik Sarveswaran and Sharath M. Shankaranarayana and Keerthi Ram and Mohanasankar Sivaprakasam},
journal={ArXiv},
year={2019},
volume={abs/1902.04099}
Jayanthi Sivaswamy, Alphin J Thottupattu, Mythri V, Raghav Mehta, R Sheelakumari, & Chandrasekharan Kesavadas. (2021, November 8).
Indian Brain Segmentation Dataset(IBSD).
Zenodo. https://doi.org/10.5281/zenodo.5656776