Better Results with Artificial INtelligence for Stroke (BRAINS)

Strokes are diagnosed using radiological imaging, which contain properties that provide information about the nature of a stroke, and the possible (beneficial) effect of treatment. However, visual evaluation is difficult and time consuming. BRAINS aims analyze these images by artificial intelligence to accurately evaluate two key parameters: collateral capacity and ischemic tissue damage.

Researchers

Partners

Publications

Journal Articles

1.

R.S. Barros, W.E. van der Steen, A.M. Boers, I. Zijlstra, R. van den Berg, W. El Youssoufi, A. Urwald, D. Verbaan, P. Vandertop, C. Majoie,; S.D. Olabarriaga

Automated segmentation of subarachnoid hemorrhages with convolutional neural networks Journal Article

Informatics in Medicine Unlocked, 19 , 2020.

Abstract | Links | BibTeX

2.

R.S. Barros, M.L. Tolhuisen, A.M. Boers. I. Jansen, E. Ponomareva, D.W. Dippel, A. van der Lugt, R.J. van Oostenbrugge, W.H. van Zwam, O.A. Berkhemer, M. Goyal, A.M. Demchuk, B.K. Menon, P. Mitchell, M.D. Hill, T.G. Jovin, A. Davalos, B.C.V. Campbell, J.L. Saver, Y.B.W.E.M. Roos, K.W. Muir, P. White, S. Bracard, F. Guillemin, S.D. Olabarriaga, C.B.L.M Majoie, H.A. Marquering

Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks Journal Article

Journal of NeuroInterventional Surgery, (015471), 2019.

Abstract | Links | BibTeX