In this dataset, different sequences are co-registered to the same anatomical template and interpolated to the same resolution of 1 mm 3. In the BraTS challenge, the participants are given multi-modal 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). Such MRI data augmentation approaches have been applied to augment other datasets as well, also acquired for different organs ( Amit et al., 2017 Nguyen et al., 2019 Oksuz et al., 2019). We discuss the brain-tumor data augmentation techniques already available in the literature, and divide them into several groups depending on their underlying concepts (section 2). Also, it is heterogeneous in the sense that it includes both low- and high-grade lesions, and the included MRI scans have been acquired at different institutions (using different MR scanners). To the best of our knowledge, the dataset used for the BraTS challenge is currently the largest and the most comprehensive brain-tumor dataset utilized for validating existent and emerging algorithms for detecting and segmenting brain tumors. In this review paper, we analyze the brain-tumor segmentation approaches available in the literature, and thoroughly investigate which techniques have been utilized by the participants of the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018). To combat the problem of limited medical training sets, data augmentation techniques, which generate synthetic training examples, are being actively developed in the literature ( Hussain et al., 2017 Gibson et al., 2018 Park et al., 2019). Additionally, the majority of manually-annotated image sets are imbalanced-examples belonging to some specific classes are often under-represented. 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 ground-truth 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 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).
Although it is possible to utilize generic priors and exploit domain-specific knowledge to help improve representations, deep features can capture very discriminative characteristics and explanatory factors of the data which could have been omitted and/or unknown for human practitioners during the process of manual feature engineering ( Bengio et al., 2013). Such techniques automatically discover the underlying data representation to build high-quality models. 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.ĭeep 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 verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. 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. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. This is a very common problem in medical image analysis, especially tumor delineation. 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. 2Silesian University of Technology, Gliwice, Polandĭata augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization.Jakub Nalepa 1,2 *, Michal Marcinkiewicz 3 and Michal Kawulok 2