GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation
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- Authors
- Bakas, S., Zeng, K., Sotiras, A., Rathore, S., Akbari, H., Gaonkar, B., Rozycki, M., Pati, S., & Davatzikos, C.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Bakas, Spyridon
- Davatzikos, Christos
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Volume
- 9556
- Pages
- 144-155
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC5513179
- OpenAlex
- W2412027445
- Cited by
- 101
- References
- 0
- Citations by year
- 2025: 4 · 2024: 5 · 2023: 9 · 2022: 15 · 2021: 16 · 2020: 17 · 2019: 16 · 2018: 12 · 2017: 3 · 2016: 4
- FWCI
- 12.92
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (U24CA189523)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- glioma segmentation, multimodal MRI, gradient boosting, tumor growth modeling, expectation-maximization algorithm, image registration, brain tumor segmentation
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