no code implementations • 11 Sep 2023 • Qinghui Liu, Elies Fuster-Garcia, Ivar Thokle Hovden, Donatas Sederevicius, Karoline Skogen, Bradley J MacIntosh, Edvard Grødem, Till Schellhorn, Petter Brandal, Atle Bjørnerud, Kyrre Eeg Emblem
In this paper, we present a novel end-to-end network capable of generating future tumor masks and realistic MRIs of how the tumor will look at any future time points for different treatment plans.
no code implementations • 27 Dec 2019 • Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel Rubin, Greg Zaharchuk
A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
no code implementations • 18 Dec 2019 • Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Kyrre Eeg Emblem, Line Brennhaug Nilsen, Cathrine Saxhaug, Anna Latysheva, Kari Dolven Jacobsen, Åslaug Helland, Greg Zaharchuk, Daniel Rubin
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.