Abstract
Three 2D networks, one for each patient-plane (axial, sagittal and coronal) plus a 3-D network were ensemble for tumor segmentation over MRI images, with final Dice scores of 0.75 for the enhancing tumor (ET), 0.81 whole tumor (WT) and 0.78 for tumor core (TC). A survival prediction model was design on Matlab, based on features extracted from the automatic segmentation. Gross tumor size and location seem to play a major role on survival prediction. A final accuracy of 0.617 was achieved.
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This work was supported by the Fundación HM, under the grant “Beca intramural 2018”.
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Marti Asenjo, J., Martinez-Larraz Solís, A. (2021). MRI Brain Tumor Segmentation Using a 2D-3D U-Net Ensemble. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_32
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