@inproceedings{b32f6d93d5914b4689477fa9c08f63b9,
title = "Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRI",
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.",
keywords = "3D U-Net, Attention block, Brain tumor segmentation, Deep learning",
author = "Xue Feng and Harrison Bai and Daniel Kim and Georgios Maragkos and Jan Machaj and Ryan Kellogg",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Brain Lesion Workshop, BrainLes 2021, held in conjunction with the Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2022",
doi = "10.1007/978-3-031-09002-8_8",
language = "English (US)",
isbn = "9783031090011",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "90--96",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
address = "Germany",
}