Sub-cortical structure segmentation database for young population

TL;DR

This paper introduces a new publicly available MRI dataset of 114 young healthy subjects with manually labeled sub-cortical structures, facilitating deep learning research in brain segmentation.

Contribution

It provides a high-quality, manually annotated MRI dataset specifically for sub-cortical brain structures in young populations, which was previously lacking.

Findings

Deep learning methods achieve accurate segmentation on this dataset.

The dataset enables improved training and evaluation of brain segmentation algorithms.

Manual delineations by experts ensure high annotation quality.

Abstract

Segmentation of sub-cortical structures from MRI scans is of interest in many neurological diagnosis. Since this is a laborious task machine learning and specifically deep learning (DL) methods have become explored. The structural complexity of the brain demands a large, high quality segmentation dataset to develop good DL-based solutions for sub-cortical structure segmentation. Towards this, we are releasing a set of 114, 1.5 Tesla, T1 MRI scans with manual delineations for 14 sub-cortical structures. The scans in the dataset were acquired from healthy young (21-30 years) subjects ( 58 male and 56 female) and all the structures are manually delineated by experienced radiology experts. Segmentation experiments have been conducted with this dataset and results demonstrate that accurate results can be obtained with deep-learning methods. Our sub-cortical structure segmentation dataset, Indian Brain Segmentation Dataset (IBSD) is made openly available at \url{https://doi.org/10.5281/zenodo.5656776}.