Abstract
Brain disorders are increasingly prevalent today, making accurate brain segmentation essential for effective treatment andrecovery. This paperintroducesanenhancedunsupervisedgraph-basedbrainsegmentationmethod that employs an ellipsoid to select the nodes forming the graph. The method was rigorously evaluated on T1 and T2 modalities using four diverse datasets: the complete NFBS dataset, 48 MRIs from the IXI dataset, 16 images featuring infant data from the QIN dataset, and 36 images from the FMS dataset. Comparative analysis with two widely used state-of-the-art approaches, BET2 and BSE, revealed that the proposed method significantly improved segmentation results. On the infant dataset, the method achieved a 21% increase in sensitivity compared to BSE, along with a 14% improvement in precision and a 13% increase in the Jaccard index compared to BET2. On the NFBS dataset, it demonstrated a 10% improvement in precision over BET2. However, on the T2-weighted dataset, only slight improvements were observed compared to both BSE and BET2. This advancement in segmentation techniques holds promise for better diagnosis and treatment of various brain disorders, potentially leading to improved patient outcomes and more efficient clinical workflows.
Citare
@Inproceedings{Popa2024ImprovingUG,
author = {Maria Popa and A. Andreica},
booktitle = {International Workshop on Informatics & Data-Driven Medicine},
title = {Improving Unsupervised Graph-Based Skull Stripping: Enhancements and Comparative Analysis With State-Of-The-Art Methods},
year = {2024}
}