The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Elastix: a toolbox for intensity-based medical image registration
Klein, S., Staring, M., Murphy, K., Viergever, M. A., & Pluim, J. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2035616 · Cited by 4,311
Cited by
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge
Carass, A., Roy, S., Jog, A., Cuzzocreo, J. L., Magrath, E., Gherman, A., Button, J., Nguyen, J., Prados, F., Sudre, C. H., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.064 · Cited by 357
Self-citationBig data analysis for brain tumor detection: Deep convolutional neural networks
Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.065 · Cited by 327
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
Raghavendra, U., Acharya, U. R., & Adeli, H. (2019) · European Neurology · doi:10.1159/000504292 · Cited by 174
Griffis, J. C., Allendorfer, J. B., & Szaflarski, J. P. (2016) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.09.019 · Cited by 154
Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
Maier, O., Schröder, C., Forkert, N. D., Martinetz, T., & Handels, H. (2015) · PLOS ONE · doi:10.1371/journal.pone.0145118 · Cited by 150
Self-citation
Metadata
- Authors
- Maier, O., Wilms, M., von der Gablentz, J., Krämer, U. M., Münte, T. F., & Handels, H.
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 240
- Pages
- 89-100
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2072957471
- Cited by
- 162
- References
- 1
- FWCI
- 8.90
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. https://app.cochat.ai/papers/10.1016/j.jneumeth.2014.11.011
- The paper
- Maier, O., Wilms, M., von der Gablentz, J., Krämer, U. M., Münte, T. F., & Handels, H. (2015). Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2014.11.011
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.