UCSF Radiation-Induced Microbleed 7 T SWI Set
UCSF 7 T SWI dataset of radiation-induced cerebral microbleeds in glioma patients
91 7 T SWI scans of 73 glioma patients with radiation-induced cerebral microbleeds at UCSF, with 2,835 true microbleeds labelled among detector candidates. Described in a 2019 paper; no data release or sharing route.
Overview
This dataset comes from the Lupo lab at the University of California San Francisco and was used to train a 3D deep residual network that separates true cerebral microbleeds from false positive candidates produced by an earlier, rule-based detector. All patients had gliomas and had received brain radiotherapy. The data are described in a 2019 paper only; the paper has no data availability statement, and no download or request process has been published.
Composition
73 glioma patients, each treated with radiation to a maximum dose of 50 to 60 Gy and each with confirmed radiation-induced microbleeds. Twelve patients were scanned more than once, giving 91 scans. The paper split the patients into 54 for training, 7 for validation and 12 for testing. The paper does not report age or sex.
Acquisition
All scans were acquired at 7 T on a GE scanner with an 8- or 32-channel phased-array head coil. 31 patients had a 4-echo 3D TOF-SWI sequence (TE 2.4, 12, 14.3 and 20.3 ms, TR 40 ms, 0.5 x 0.5 x 1 mm) and 49 had a standard flow-compensated 3D SWI sequence (TE 16 ms, TR 50 ms, 0.5 x 0.5 x 2 mm). These two numbers add up to more than 73, which the paper does not explain.
Annotations
The earlier detector proposed 19,762 candidates. A research scientist experienced in reading microbleeds, guided beforehand by a neuroradiologist, marked each candidate as a true microbleed or a false positive with a labelling tool, yielding 2,835 true microbleeds and 16,927 false positives. A neuroradiologist's ratings were used separately to check the network's likelihood scores. The labels are candidate-level decisions, not voxel masks.
Known limitations
- No data release, license or data use terms exist; access, if any, is at the authors' discretion.
- The population is narrow: glioma patients after radiotherapy, scanned at 7 T, which differs from the usual 1.5 T and 3 T clinical setting.
- Ground truth comes from a single reader.
- The publisher issued a correction stating the article was published open access by mistake.
Cohort
Aggregate numbers from the sources below. Bars are relative to the 73 subjects.
Contrast / sequence
subjects, values can overlap
- Susceptibility-weighted 73 100%
Condition
subjects, values can overlap
- Cerebral microbleeds 73 100%
- Glioma 73 100%
Scanner vendor
subjects
- GE HealthCare 73 100%
Field strength
subjects
- 7 T 73 100%
Split
subjects
- Training 54 74%
- Test 12 16%
- Validation 7 10%
License and access
Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer
Ask the authors or the data holder. No published process or terms, and access is at their discretion
No published license or data use terms
The data holder has published no license and no data use terms. Any use, sharing or commercial right has to be agreed with the data holder, and nothing can be assumed to be allowed. Default copyright and data protection law still apply.
The paper has no data availability statement and no data sharing route is published. The corresponding author is Janine Lupo, UCSF.
What you can do
- Commercial use Not stated
- Train ML models Not stated
- Create derived data Not stated
- Publish results Not stated
What you can share
- Share the data Not stated
- Share derived data Not stated
- Share trained models Not stated
What you must do
- Cite or credit Not stated
- Share alike Not stated
- Sign an agreement Not stated
- Ethics approval Not stated
- Manuscript review Not stated
- Release code Not stated
- Return results Not stated
- Delete after use Not stated
Limits
- No re-identification Not stated
- Location limits Not stated
Citation
Chen Y, Villanueva-Meyer JE, Morrison MA, Lupo JM. Toward Automatic Detection of Radiation-Induced Cerebral Microbleeds Using a 3D Deep Residual Network. J Digit Imaging 32(5), 766-772 (2019). doi:10.1007/s10278-018-0146-z
All numbers
Every number on this page, as stored in stats.csv, with its source.
| Measure | Breakdown | Value | Source |
|---|---|---|---|
| Subjects | total glioma patients after radiotherapy with confirmed radiation-induced microbleeds | 73 | chen2019 Subjects and Image Acquisition |
| Scans | total 12 patients had serial imaging | 91 | chen2019 Subjects and Image Acquisition |
| Subjects | condition=cerebral_microbleeds | 73 | chen2019 Subjects and Image Acquisition |
| Subjects | condition=glioma | 73 | chen2019 Subjects and Image Acquisition |
| Subjects | contrast=swi 31 patients scanned with 4-echo 3D TOF-SWI and 49 with standard SWI; the paper does not explain the overlap | 73 | chen2019 Subjects and Image Acquisition |
| Subjects | field_strength=7 | 73 | chen2019 Subjects and Image Acquisition |
| Subjects | vendor=ge | 73 | chen2019 Subjects and Image Acquisition |
| Subjects | split=train | 54 | chen2019 Table 1 |
| Subjects | split=validation | 7 | chen2019 Table 1 |
| Subjects | split=test | 12 | chen2019 Table 1 |
Sources
The keys used in the table above.
- chen2019 Chen et al. 2019, Journal of Digital Imaging (PMC6737152) paper