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Stable Atlas-based Mapped Prior (STAMP) Machine-learning Segmentation for Multicenter Large-scale MRI Data

Institution:
1Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA. Electronic address: eunyoung-kim@uiowa.edu.
2Department of Biomedical Engineering, University of Iowa, Iowa City, IA, USA.
3Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, IA, USA.
Publisher:
Elsevier Science
Publication Date:
Sep-2014
Journal:
Magn Reson Imaging
Volume Number:
32
Issue Number:
7
Pages:
832-44
Citation:
Magn Reson Imaging. 2014 Sep;32(7):832-44.
PubMed ID:
24818817
PMCID:
PMC4099271
Keywords:
Machine-Learning, Multi-site Study, Random Forest, Segmentation
Appears in Collections:
NA-MIC, SPL
Sponsors:
P41 RR015241/RR/NCRR NIH HHS/United States
R01 EB000975/EB/NIBIB NIH HHS/United States
R01 EB008171/EB/NIBIB NIH HHS/United States
R01 NS040068/NS/NINDS NIH HHS/United States
R01 NS050568/NS/NINDS NIH HHS/United States
R01 NS054893/NS/NINDS NIH HHS/United States
R03 EB008673/EB/NIBIB NIH HHS/United States
S10 RR023392/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Kim E.Y., Magnotta V.A., Liu D., Johnson H.J. Stable Atlas-based Mapped Prior (STAMP) Machine-learning Segmentation for Multicenter Large-scale MRI Data. Magn Reson Imaging. 2014 Sep;32(7):832-44. PMID: 24818817. PMCID: PMC4099271.
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Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML-algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a stable atlas-based mapped prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n > 3000). Less than 10% of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen.

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