Abstract
In this study, we focus on Task 1 of the 2021 Multimodal Brain Tumor Segmentation (BraTS) challenge. We present a modified U-net model aimed at improving the segmentation of glioblastomas, reducing the computation time without compromising detection sensitivity. Our automated approach takes multimodal MR images as input, generates a bounding box of the brain volume, and combines the model predictions at the 2D slice level into a full 3D segmentation that is written into a NIfTI file. On the official 2021 BraTS test set of 570 cases, the model obtained median Dice scores of 0.80, 0.87, and 0.87, as well as median 95% Hausdorff distances of 2.45, 4.64, and 6.40 for the enhancing tumor, tumor core, and whole tumor regions, respectively.
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Yan, B.B. et al. (2022). MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_7
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DOI: https://doi.org/10.1007/978-3-031-09002-8_7
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