Data Augmentation via Image Registration

Nalepa, Jakub; Cwiek, Marcin; Dudzik, Wojciech; Kawulok, Michał; Hayball, Michael P.; Mrukwa, Grzegorz; Piechaczek, Szymon; Lorenzo, Pablo Ribalta; Marcinkiewicz, M.; Bobek-Billewicz, Barbara; Wawrzyniak, Pawel; Ulrych, Paweł; Szymanek, Janusz

doi:10.1109/icip.2019.8803423

Cited by 29 publications

(17 citation statements)

References 15 publications

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“…Examples of brain-tumor images generated using diffeomorphic registration are given in Figure 4-such artificially-generated data significantly improved the abilities of deep learners, especially when combined with affine transformations, as we showed in Nalepa et al (2019a). The generated (I ′ ) images preserve topological information of the original image data (I) with subtle changes to the tissue.…”

Section: Data Augmentation Using Elastic Image Transformationsmentioning

confidence: 74%

“…Test-time data augmentation exploited by Wang et al (2018) not only decreased DICE for the whole-tumor segmentation, but also caused the increase of the correspoding Hausdorff distance. The results come from our paper (Nalepa et al, 2019a). The best results are boldfaced.…”

Section: Brats 2018 Challengementioning

confidence: 99%

“…Similarly, we applied diffeomorphic image registration (DIR) coupled with a recommendation algorithm 6 to select training image pairs for registration in the data augmentation process (Nalepa et al, 2019a). The proposed augmentation was compared with random horizontal flipping, and the experiments indicated that the combined approach leads to statistically significant (Wilcoxon test at p < 0.01) improvements in DICE (Table 6).…”

Section: Beyond the Brats Challengementioning

confidence: 99%

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Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

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245

127

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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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Section: Data Augmentation Using Elastic Image Transformationsmentioning

confidence: 74%

Section: Brats 2018 Challengementioning

confidence: 99%

Section: Beyond the Brats Challengementioning

confidence: 99%

See 1 more Smart Citation

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

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245

127

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show abstract

“…23,39,44,45,92,95,[118][119][120] Krivov et al 26 developed a method wherein brain lesions could be mapped onto healthy patient scans, by performing deformable image registration between the original patient and a healthy one. Nalepa et al 121 demonstrated an approach using diffeomorphic image registration, co-registering pairs of lesion images to create augmented data which improved the generalisation of their DL models when combined with an affine augmentation.…”

Section: Spline Interpolationmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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“…Enough amount of data used for learning and testing increases the accuracy of the training stage [4][5][6]. Hence, learning-based algorithms may be capable of detecting small details and differences [7][8][9]. Moreover, the qualities and content of the datasets differ according to the aim of the studies.…”

Section: Introductionmentioning

confidence: 99%