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Scientific article
Open access
English

Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm

Published inPattern Analysis and Application, vol. 19, p. 579-591
Publication date2016
Abstract

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice's index of [Formula: see text] ([Formula: see text]) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.

Keywords
  • Classification
  • MRI
  • Segmentation
  • Supervised learning
Citation (ISO format)
MAGLIETTA, Rosalia et al. Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm. In: Pattern Analysis and Application, 2016, vol. 19, p. 579–591. doi: 10.1007/s10044-015-0492-0
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ISSN of the journal1433-7541
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