@inproceedings{27313453dca14c2d8ae92f84898bd369,
title = "Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation",
abstract = "Tumor segmentation is an important research topic in medical image segmentation. With the fast development of deep learning in computer vision, automated segmentation of brain tumors using deep neural networks becomes increasingly popular. U-Net is the most widely-used network in the applications of automated image segmentation. Many well-performed models are built based on U-Net. In this paper, we devise a model that combines the variational-autoencoder regularuzed 3D U-Net model [10] and the MultiResUNet model [7]. The model is trained on the 2020 Multimodal Brain Tumor Segmentation Challenge (BraTS) dataset and predicts on the validation set. Our result shows that the modified 3D MultiResUNet performs better than the previous 3D U-Net.",
keywords = "3D U-Net, Brain tumor segmentation, MultiResUNet, Variational-autoencoder regularization",
author = "Jiarui Tang and Tengfei Li and Hai Shu and Hongtu Zhu",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",
year = "2021",
doi = "10.1007/978-3-030-72087-2_38",
language = "English (US)",
isbn = "9783030720865",
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 = "431--440",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
address = "Germany",
}