Sub-cortical structure segmentation database for young population
This provides a domain-specific resource for neurological diagnosis research, but it is incremental as it focuses on a new dataset for an existing task.
The authors tackled the lack of a high-quality dataset for sub-cortical structure segmentation in MRI scans by releasing the Indian Brain Segmentation Dataset (IBSD), containing 114 manually delineated scans, and demonstrated that deep-learning methods can achieve accurate results on it.
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}.