Abstract
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.
Highlights
Deep learning has established the state of the art in many sub-areas of computer vision and pattern recognition (Krizhevsky et al, 2017), including medical imaging and medical image analysis (Litjens et al, 2017)
We have focused on the wholetumor segmentation, as it was an intermediate step in the automated dynamic contrast-enhanced magnetic resonance imaging (MRI) analysis, in which perfusion parameters have been extracted for the entire tumor volume
We reviewed the state-of-the-art data augmentation methods applied in the context of segmenting brain tumors from MRI
Summary
Deep learning has established the state of the art in many sub-areas of computer vision and pattern recognition (Krizhevsky et al, 2017), including medical imaging and medical image analysis (Litjens et al, 2017). In order to successfully build well-generalizing deep models, we need huge amount of ground-truth data to avoid overfitting of such large-capacity learners, and “memorizing” training sets (LeCun et al, 2016) It has become a significant obstacle which makes deep neural networks quite challenging to apply in the medical image analysis field where acquiring high-quality groundtruth data is time-consuming, expensive, and very human-dependent, especially in the context of brain-tumor delineation from magnetic resonance imaging (MRI) (Isin et al, 2016; Angulakshmi and Lakshmi Priya, 2017; Marcinkiewicz et al, 2018; Zhao et al, 2019). In the BraTS challenge, the participants are given multimodal MRI data of brain-tumor patients (as already mentioned, both low- and high-grade gliomas), alongside the corresponding ground-truth multi-class segmentation (section 3) In this dataset, different sequences are co-registered to the same anatomical template and interpolated to the same resolution of 1 mm.
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