Automated graph-based analysis and correction of cortical volume topology
Abstract
The human cerebral cortex is topologically equivalent to a sheet and can be considered topologically spherical if it is closed at the brain stem. Low-level segmentation of magnetic resonance (MR) imagery typically produces cerebral volumes whose tessellations are not topologically spherical. We present a novel algorithm that analyzes and constrains the topology of a volumetric object. Graphs are formed that represent the connectivity of voxel segments in the foreground and background of the image. These graphs are analyzed and minimal corrections to the volume are made prior to tessellation. We apply the algorithm to a simple test object and to cerebral white matter masks generated by a low-level tissue identification sequence. We tessellate the resulting objects using the marching cubes algorithm and verify their topology by computing their Euler characteristics. A key benefit of the algorithm is that it localizes the change to a volume to the specific areas of its topological defects.
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An extensively validated tissue classifier generated detailed maps of GM, white matter, and cerebrospinal fluid.
“An extensively validated tissue classifier generated detailed maps of GM, white matter, and cerebrospinal fluid by using a Gaussian mixture distribution to generate a maximum a posteriori segmentation of the data (23, 24)”
From Dynamic mapping of human cortical development during childhood through early adulthood (2004) · cites this paper as [23]
The Preprocessing passage (B. Human MR Data Sets, p. 7) says brain voxels were labeled as white matter, grey matter, CSF, and partial-volume combinations using a Bayesian classifier. It does not describe the classifier as extensively validated or specify a Gaussian mixture distribution or maximum a posteriori segmentation; the reported validation on p. 9 concerns the topology-correction result, not the tissue classifier. (LLM, conf=0.87)
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- Authors
- Shattuck, D. W., & Leahy, R. M.
- Affiliations
- University of Southern California (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2001
- Volume
- 20
- Issue
- 11
- Pages
- 1167-1177
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2023750321
- Cited by
- 215
- References
- 31
- Citations by year
- 2026: 3 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 3 · 2019: 7 · 2018: 11 · 2017: 2 · 2016: 8 · 2015: 5 · 2014: 7 · 2013: 11 · 2012: 15
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (R01-MH53213, R01MH053213)
- National Center for Research Resources (P41RR013642, P41 RR13642)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Image Processing Techniques
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- topological defects, cerebral cortex, spherical topology, marching cubes, Euler characteristic, 3D image segmentation, brain MRI
- MeSH terms
- Algorithms, Electronic Data Processing, Brain Mapping, Cerebral Cortex, Computer Graphics, Humans, Magnetic Resonance Imaging, Statistics as Topic
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