Abstract
Datasets for medical image segmentation usually contain a very limited number of training examples. However, deep learning methods prove to be very competitive for such data analysis problems. Surprisingly, quite limited data augmentation is used during training. We presume that it’s due to historical reasons: standardization and normalization of medical images dominate over methods for increasing the size of a training set by artificial transformation of images. We assume that it is partly caused by the absence of methods which preserve properties of adequately preprocessed medical images. In this paper, we propose a new method for brain MRI augmentation, which allows us to map a lesion from an original image to a healthy brain. We compare the performance of U-Net and DeepMedic, two popular deep learning architectures, using the proposed method, a set of classical image augmentation methods, and a combination of both approaches. Our results suggest that at least one of the individual strategies, as well as their combination, provide an increase in accuracy of brain lesions segmentation if the training sample is relatively small.
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Notes
- 1.
Code is publicly available at https://github.com/neuro-ml/deep_pipe.
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Acknowledgments
The data used in preparing this paper were obtained from the Alzheimer‘s Disease Neuroimaging Initiative (ADNI) database. A complete listing of ADNI investigators and imaging protocols may be found at adni.loni.usc.edu.
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Krivov, E., Pisov, M., Belyaev, M. (2018). MRI Augmentation via Elastic Registration for Brain Lesions Segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017. Lecture Notes in Computer Science(), vol 10670. Springer, Cham. https://doi.org/10.1007/978-3-319-75238-9_32
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