@inproceedings{33c6925180a142a29f642dcdce07e236,
title = "Joint Image Quality Assessment and Brain Extraction of Fetal MRI Using Deep Learning",
abstract = "Quality assessment (QA) and brain extraction (BE) are two fundamental steps in 3D fetal brain MRI reconstruction and quantification. Conventionally, QA and BE are performed independently, ignoring the inherent relation of the two closely-related tasks. However, both of them focus on the brain region representation, so they can be jointly optimized to ensure the network to learn shared features and avoid overfitting. To this end, we propose a novel multi-stage deep learning model for joint QA and BE of fetal MRI. The locations and orientations of fetal brains are randomly variable, and the shapes and appearances of fetal brains change remarkably across gestational ages, thus imposing great challenges to extract shared features of QA and BE. To address these problems, we firstly design a brain detector to locate the brain region. Then we introduce the deformable convolution to adaptively adjust the receptive field for dealing with variable brain shapes. Finally, a task-specific module is used for image QA and BE simultaneously. To obtain a well-trained model, we further propose a multi-step training strategy. We cross validate our method on two independent fetal MRI datasets acquired from different scanners with different imaging protocols, and achieve promising performance.",
keywords = "Brain extraction, Fetal MRI, Quality assessment",
author = "Lufan Liao and Xin Zhang and Fenqiang Zhao and Tao Zhong and Yuchen Pei and Xiangmin Xu and Li Wang and He Zhang and Dinggang Shen and Gang Li",
note = "Funding Information: Acknowledgements. XZ and XX are supported in part by the NSFC under grant U1801262, Guangzhou Key Laboratory of Body Data Science under grant 201605030011. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59725-2_40",
language = "English",
isbn = "9783030597245",
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 = "415--424",
editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",
address = "Germany",
}