Data Augmentation for Brain-Tumor Segmentation: A Review
Open accessAbstract
Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.
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Nalepa et al. reviewed current techniques for data augmentation using MRI images, focusing on papers submitted to the multimodal brain tumor segmentation challenge BraTS 2018.
“Nalepa et al. [11] reviewed current techniques for data augmentation using MRI images.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [11]
Few studies have reviewed deep and federated learning, and these studies have limitations.
“Few studies have reviewed deep and federated learning. Moreover, these studies have limitations.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [11]
AI-based methods are used for brain tumor detection because of their outstanding results.
“AI-based methods are used for brain tumor detection because of their outstanding results [11].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [11]
Nalepa et al.'s review did not include unpublished, combined datasets and images collected from the internet.
“Information on unpublished, combined datasets and images collected from the internet were not included in the study.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [11]
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- Authors
- Nalepa, J., Marcinkiewicz, M., & Kawulok, M.
- Affiliations
- Silesian University of Technology (PL)
- Corresponding author
- Nalepa, Jakub
- ORCID
- Nalepa, Jakub
- Venue
- Frontiers in Computational Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2019-12-11
- Volume
- 13
- Pages
- 83-83
- ISSN
- 1662-5188 (ISSN-L)
- PMC
- PMC6917660
- OpenAlex
- W2992511782
- Cited by
- 340
- References
- 80
- Citations by year
- 2026: 31 · 2025: 42 · 2024: 61 · 2023: 85 · 2022: 66 · 2021: 40 · 2020: 15
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Computational Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Narodowe Centrum Badan i Rozwoju (POIR.01.02.00-00-0030/15)
- 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
- data augmentation, brain tumor segmentation, magnetic resonance imaging, BraTS 2018, medical image analysis, synthetic data generation, generalization, deep neural network, tumor delineation
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 ›
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