Abstract
In the study, we used three two-dimensional convolutional neural networks, including SegNet, U-Net, and PSP-Net, to design an automatic segmentation of brain tumor from three-dimensional MR datasets. We extracted 2D slices from three slice orientations as the input tensor of the network in the training stage. In the prediction stage, we predict a volume several times with slicing along different angles. Based on the results, we learned that the result predicted more times has better outcomes than those predicted less times. Also, we implement two ensemble methods to combine the result of the three networks. According to the results, the above strategies all contributed to the improvement of the accuracy of segmentation.
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Weng, YT., Chan, HW., Huang, TY. (2020). Automatic Segmentation of Brain Tumor from 3D MR Images Using SegNet, U-Net, and PSP-Net. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_22
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DOI: https://doi.org/10.1007/978-3-030-46643-5_22
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