publication . Article . 2016

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

Maglietta R; Amoroso N; Boccardi M; Bruno S; Chincarini A; Gb, Frisoni; Paolo Inglese; Redolfi A; Tangaro S; Tateo A; ...
Open Access English
  • Published: 01 Jan 2016
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 ...
Subjects
free text keywords: Classification, MRI, Segmentation, Supervised learning, ddc:616.89, Artificial Intelligence, Computer Vision and Pattern Recognition, Industrial and Commercial Application, Artificial Intelligence & Image Processing, 0801 Artificial Intelligence And Image Processing, 0802 Computation Theory And Mathematics, 0906 Electrical And Electronic Engineering
Funded by
CIHR
Project
  • Funder: Canadian Institutes of Health Research (CIHR)
,
NIH| Alzheimers Disease Neuroimaging Initiative
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funding stream: NATIONAL INSTITUTE ON AGING
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