Contusion segmentation from subjects with Traumatic Brain Injury: A random forest framework
Abstract
Traumatic Brain Injury (TBI) occurs when a sudden injury causes trauma to the brain. Contusions are one of the most common types of lesion that arise after TBI, and they can be observed on a subject's MRI or CT. Since it is hypothesised that indices such as contusion load may be potential biomarkers for TBI, the ability to segment contusions is highly desirable. Currently, we are not aware of any fully automated methods that address this segmentation task. In this paper we present a completely automated random-forest based approach to contusion segmentation that uses multi-modality MRI. Given a training set of MR images and ground-truth segmentations, a set of features is derived for each voxel that describe both the local neighbourhood and longer-range contextual information in the images. A random forest is trained using these features and the ground-truth voxel labels, and used to produce an automatic contusion segmentation of an unseen test subject. We evaluate the method using 6-fold cross-validation on a dataset consisting of 23 subjects, obtaining a mean DICE overlap of 0.60.
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We improved over the reported performance of the pipeline in Rao et al. (2014).
“As a comparison, we improved over the reported performance of the pipeline in Rao et al. (2014) .”
From Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2017) · cites this paper as Rao et al. (2014)
Our system is capable of segmenting even small and diffused pathologies, whereas Rao et al. focused only on contusions.
“Important to note is that the latter work focused only on segmentation of contusions, while our system has been shown capable of segmenting even small and diffused pathologies.”
From Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation (2017) · cites this paper as Rao et al. (2014)
Cites
Position statement: definition of traumatic brain injury
Menon, D. K., Schwab, K., Wright, D. W., & Maas, A. I. (2010) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2010.05.017 · Cited by 1,678 · see citing works
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Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · see citing works
“Rao et al. (2014) also incorporate brain structure segmentation results obtained from a multi-atlas label propagation approach (Ledig et al. (2015)) to provide strong tissue-class priors to the Random Forests.”
MethodResultSelf-citationBrain tumor detection using fusion of hand crafted and deep learning features
Saba, T., Sameh Mohamed, A., El-Affendi, M., Amin, J., & Sharif, M. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.09.007 · Cited by 433 · see citing works
- Authors
- Rao, A., Ledig, C., Newcombe, V., Menon, D., & Rueckert, D.
- Affiliations
- Imperial College London (GB)
- University of Cambridge (GB)
- Venue
- 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2014-04
- Pages
- 333-336
- OpenAlex
- W2044267109
- Cited by
- 18
- References
- 1
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 4 · 2022: 2 · 2021: 2 · 2020: 1 · 2019: 3 · 2018: 1 · 2016: 1 · 2014: 2
- FWCI
- 0.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Keywords
- traumatic brain injury, cerebral contusion, random forest, multimodal MRI, automated image segmentation, biomarkers, voxel features, contextual features
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