Harmonization Benchmarking Tool for Neuroimaging Datasets
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908103215153152 |
|---|---|
| author | Osika, Tom Ebrahim, Ebrahim Styner, Martin Niethammer, Marc Sawyer, Thomas Enquobahrie, Andinet |
| author_facet | Osika, Tom Ebrahim, Ebrahim Styner, Martin Niethammer, Marc Sawyer, Thomas Enquobahrie, Andinet |
| contents | A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms. There is a wide variety of data harmonization techniques, but there are few tools that streamline the process of harmonizing data, comparing across techniques, and benchmarking new techniques. In this paper, we introduce HArmonization BEnchmarking Tool (HABET), an open source tool for generating harmonized images and evaluating the performance of different harmonization algorithms. To demonstrate the capabilities of HABET, we harmonize diffusion MRI images from the Adolescent Brain and Cognitive Development (ABCD) study using two different approaches, and we compare their performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_07869 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Harmonization Benchmarking Tool for Neuroimaging Datasets Osika, Tom Ebrahim, Ebrahim Styner, Martin Niethammer, Marc Sawyer, Thomas Enquobahrie, Andinet Image and Video Processing Quantitative Methods A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms. There is a wide variety of data harmonization techniques, but there are few tools that streamline the process of harmonizing data, comparing across techniques, and benchmarking new techniques. In this paper, we introduce HArmonization BEnchmarking Tool (HABET), an open source tool for generating harmonized images and evaluating the performance of different harmonization algorithms. To demonstrate the capabilities of HABET, we harmonize diffusion MRI images from the Adolescent Brain and Cognitive Development (ABCD) study using two different approaches, and we compare their performance. |
| title | Harmonization Benchmarking Tool for Neuroimaging Datasets |
| topic | Image and Video Processing Quantitative Methods |
| url | https://arxiv.org/abs/2211.07869 |