1 brain images
Brain images are acquired from different modalities, such as Magnetic Resonance Image (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), Ultrasound etc. The image from different modalities always be saved and archived in Digital Imaging and Communications in Medicine (DICOM) format. DICOM files are easy to handle, store, print, and transmit.
For medical analysis in supervised learning, images must be labelled manually by medical specialist or radiologist. Also, it is difficult to obtain access to these images because medical images are protected considering the privacy of the patient. Therefore, Increasing the number of brain images for training in deep learning must solve this problem, and is more difficult than natural image.
2 datasets
There are several public datasets in brain images for different diseases and modalities. We describe them in this section, including samples sizes, type of data, and the aim of building the datasets.
BRATS benchmark. The Multimodal Brain Tumor Image Segmentation (BRATS)[1].
ISLES benchmark. The Ischemic Stroke Lesion Segmentation (ISLES)[2].
ANDI Alzheimer’s Disease Neuroimaging Initiative (ANDI)[3].
ABIDE I and ABIDE II. Autism Brain Imaging Data Exchange I and II[4, 5]
IBSR Internet Brain Segmentation Repository[6].
LPBA40. LONI Probabilistic Brain Atlas Project [7, 8]
OASIS Open Access Series of Imaging Studies[9].
The Cancer Imaging Archive. TCIA is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download[10]. TCGA The Cancer Genome Atlas[11], publicly available in the TCIA
References
[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, and R. Wiest, “The multimodal brain tumor image segmentation benchmark (BRATS),” IEEE transactions on medical imaging, vol. 34, no. 10, pp. 1993, 2015.
[2] O. Maier, B. H. Menze, J. von der Gablentz, L. Häni, M. P. Heinrich, M. Liebrand, S. Winzeck, A. Basit, P. Bentley, and L. Chen, “ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI,” Medical image analysis, vol. 35, pp. 250-269, 2017.
[3] C. R. Jack, M. A. Bernstein, N. C. Fox, P. Thompson, G. Alexander, D. Harvey, B. B. Rtr, P. J. Britson, J. L. Whitwell, and B. A. Chadwick Ward, “The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods,” Journal of Magnetic Resonance Imaging, vol. 27, no. 4, pp. 685-691, 2010.
[4] A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, and M. Dapretto, “The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism,” Molecular psychiatry, vol. 19, no. 6, pp. 659, 2014.
[5] A. Di Martino, D. O’Connor, B. Chen, K. Alaerts, J. S. Anderson, M. Assaf, J. H. Balsters, L. Baxter, A. Beggiato, and S. Bernaerts, “Enhancing studies of the connectome in autism using the autism brain imaging data exchange II,” Scientific data, vol. 4, pp. 170010, 2017.
[6] T. Rohlfing, “Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable,” IEEE Transactions on Medical Imaging, vol. 31, no. 2, pp. 153-63, 2012.
[7] D. W. Shattuck, M. Mirza, V. Adisetiyo, C. Hojatkashani, G. Salamon, K. L. Narr, R. A. Poldrack, R. M. Bilder, and A. W. Toga, “Construction of a 3D probabilistic atlas of human cortical structures,” Neuroimage, vol. 39, no. 3, pp. 1064-1080, 2008.
[8] D. W. Shattuck, G. Prasad, M. Mirza, K. L. Narr, and A. W. Toga, “Online Resource for Validation of Brain Segmentation Methods,” Neuroimage, vol. 45, no. 2, pp. 431-439, 2009.
[9] D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, “Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,” Journal of Cognitive Neuroscience, vol. 19, no. 9, pp. 1498-507, 2007.
[10] "The Cancer Imaging Archive," 2018; https://www.cancerimagingarchive.net/.
[11] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos, “Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features,” Scientific Data, vol. 4, pp. 170117, 09/05, 2017.
（Generative adversarial network in medical imaging: A review）
