Fetal-BET: Brain Extraction Tool for Fetal MRI
<italic>Goal:</italic> In this study, we address the critical challenge of fetal brain extraction from MRI sequences. Fetal MRI has played a crucial role in prenatal neurodevelopmental studies and in advancing our knowledge of fetal brain development <italic>in-utero</italic>...
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10596549/ |
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| author | Razieh Faghihpirayesh Davood Karimi Deniz Erdogmus Ali Gholipour |
| author_facet | Razieh Faghihpirayesh Davood Karimi Deniz Erdogmus Ali Gholipour |
| author_sort | Razieh Faghihpirayesh |
| collection | DOAJ |
| description | <italic>Goal:</italic> In this study, we address the critical challenge of fetal brain extraction from MRI sequences. Fetal MRI has played a crucial role in prenatal neurodevelopmental studies and in advancing our knowledge of fetal brain development <italic>in-utero</italic>. Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it poses significant challenges due to 1) non-standard fetal head positioning, 2) fetal movements during examination, and 3) vastly heterogeneous appearance of the developing fetal brain and the neighboring fetal and maternal anatomy across gestation, and with various sequences and scanning conditions. Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. Currently, there is no method for accurate fetal brain extraction on various fetal MRI sequences. <italic>Methods:</italic> In this work, we first built a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Our dataset covers the three common MRI sequences including T2-weighted, diffusion-weighted, and functional MRI acquired with different scanners. These data include images of normal and pathological brains. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures, the attention mechanism, feature learning across multiple MRI modalities, and data augmentation for fast, accurate, and generalizable automatic fetal brain extraction. <italic>Results:</italic> Evaluations on independent test data, including data available from other centers, show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages. <italic>Conclusions:</italic>By leveraging rich information from diverse multi-modality fetal MRI data, our proposed deep learning solution enables precise delineation of the fetal brain on various fetal MRI sequences. The robustness of our deep learning model underscores its potential utility for fetal brain imaging. |
| format | Article |
| id | doaj-art-a6c7efe633f54f958ca1d6fc5bb2a0a5 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-a6c7efe633f54f958ca1d6fc5bb2a0a52025-01-30T00:03:46ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762024-01-01555156210.1109/OJEMB.2024.342696910596549Fetal-BET: Brain Extraction Tool for Fetal MRIRazieh Faghihpirayesh0https://orcid.org/0000-0002-2680-5021Davood Karimi1https://orcid.org/0000-0002-5155-2644Deniz Erdogmus2https://orcid.org/0000-0002-1114-3539Ali Gholipour3https://orcid.org/0000-0001-7699-4564Electrical and Computer Engineering Department, Northeastern University, Boston, MA, USARadiology Department, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USAElectrical and Computer Engineering Department, Northeastern University, Boston, MA, USARadiology Department, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA<italic>Goal:</italic> In this study, we address the critical challenge of fetal brain extraction from MRI sequences. Fetal MRI has played a crucial role in prenatal neurodevelopmental studies and in advancing our knowledge of fetal brain development <italic>in-utero</italic>. Fetal brain extraction is a necessary first step in most computational fetal brain MRI pipelines. However, it poses significant challenges due to 1) non-standard fetal head positioning, 2) fetal movements during examination, and 3) vastly heterogeneous appearance of the developing fetal brain and the neighboring fetal and maternal anatomy across gestation, and with various sequences and scanning conditions. Development of a machine learning method to effectively address this task requires a large and rich labeled dataset that has not been previously available. Currently, there is no method for accurate fetal brain extraction on various fetal MRI sequences. <italic>Methods:</italic> In this work, we first built a large annotated dataset of approximately 72,000 2D fetal brain MRI images. Our dataset covers the three common MRI sequences including T2-weighted, diffusion-weighted, and functional MRI acquired with different scanners. These data include images of normal and pathological brains. Using this dataset, we developed and validated deep learning methods, by exploiting the power of the U-Net style architectures, the attention mechanism, feature learning across multiple MRI modalities, and data augmentation for fast, accurate, and generalizable automatic fetal brain extraction. <italic>Results:</italic> Evaluations on independent test data, including data available from other centers, show that our method achieves accurate brain extraction on heterogeneous test data acquired with different scanners, on pathological brains, and at various gestational stages. <italic>Conclusions:</italic>By leveraging rich information from diverse multi-modality fetal MRI data, our proposed deep learning solution enables precise delineation of the fetal brain on various fetal MRI sequences. The robustness of our deep learning model underscores its potential utility for fetal brain imaging.https://ieeexplore.ieee.org/document/10596549/Deep learningbrain extractionfetal MRI |
| spellingShingle | Razieh Faghihpirayesh Davood Karimi Deniz Erdogmus Ali Gholipour Fetal-BET: Brain Extraction Tool for Fetal MRI IEEE Open Journal of Engineering in Medicine and Biology Deep learning brain extraction fetal MRI |
| title | Fetal-BET: Brain Extraction Tool for Fetal MRI |
| title_full | Fetal-BET: Brain Extraction Tool for Fetal MRI |
| title_fullStr | Fetal-BET: Brain Extraction Tool for Fetal MRI |
| title_full_unstemmed | Fetal-BET: Brain Extraction Tool for Fetal MRI |
| title_short | Fetal-BET: Brain Extraction Tool for Fetal MRI |
| title_sort | fetal bet brain extraction tool for fetal mri |
| topic | Deep learning brain extraction fetal MRI |
| url | https://ieeexplore.ieee.org/document/10596549/ |
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