Abstract
Accurate segmentation of different sub-regions of gliomas including peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from multiparametric MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness in the past several brain segmentation challenges as well as other semantic and medical image segmentation problems. In this paper we developed a deep-learning-based segmentation method using a patch-based 3D UNet with the attention block. Hyper-parameters tuning and training and testing augmentations were applied to increase the model performance. Preliminary results showed effectiveness of the segmentation model and achieved mean Dice scores of 0.806 (ET), 0.863 (TC) and 0.918 (WT) in the validation dataset.
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Feng, X., Bai, H., Kim, D., Maragkos, G., Machaj, J., Kellogg, R. (2022). Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRI. 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_8
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DOI: https://doi.org/10.1007/978-3-031-09002-8_8
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