A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation
Open accessAbstract
We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al., 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al., 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al., 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.
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Multi-atlas segmentation has been applied successfully to many challenging segmentation problems such as identifying hippocampal subfields in MRI, the spinal cord, and the mitral valve in preoperative 3D echocardiography.
“This approach has been applied successfully to many challenging segmentation problems 15 such as identifying hippocampal subfields in magnetic resonance images (MRI) of the brain, 17,18 the spinal cord, 19 and the mitral valve in preoperative 3D echocardiography. 20”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [18]
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- Authors
- Wang, H., Das, S. R., Suh, J. W., Altinay, M., Pluta, J., Craige, C., Avants, B., & Yushkevich, P. A.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Wang, Hongzhi
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 55
- Issue
- 3
- Pages
- 968-985
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3049832
- OpenAlex
- W2053383165
- Cited by
- 177
- References
- 50
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 7 · 2023: 5 · 2022: 8 · 2021: 9 · 2020: 11 · 2019: 9 · 2018: 21 · 2017: 18 · 2016: 16 · 2015: 13 · 2014: 18 · 2013: 13 · 2012: 14
- FWCI
- 18.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging (R21 NS061111)
- National Institute of Mental Health
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- wrapper methods, brain MRI segmentation, hippocampus segmentation, brain extraction, brain tissue segmentation, white matter segmentation, gray matter segmentation, multi-atlas label fusion, FreeSurfer, BET, spark plasma sintering, mild cognitive impairment
- MeSH terms
- Aged, Algorithms, Alzheimer Disease, Artificial Intelligence, Atlases as Topic, Brain, Cerebral Cortex, Female, Hippocampus, Humans, Image Enhancement, Image Processing, Computer-Assisted, Male, Middle Aged, Software, Databases, Factual
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