The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Automated brain extraction of multisequence MRI using artificial neural networks
Open accessAbstract
Brain extraction is a critical preprocessing step in the analysis of neuroimaging studies conducted with magnetic resonance imaging (MRI) and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD‐BET) relying on artificial neural networks that aim to overcome these limitations. We demonstrate that HD‐BET outperforms six popular, publicly available brain extraction algorithms in several large‐scale neuroimaging datasets, including one from a prospective multicentric trial in neuro‐oncology, yielding state‐of‐the‐art performance with median improvements of +1.16 to +2.50 points for the Dice coefficient and −0.66 to −2.51 mm for the Hausdorff distance. Importantly, the HD‐BET algorithm, which shows robust performance in the presence of pathology or treatment‐induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility, the HD‐BET prediction algorithm is made freely available (www.neuroAI-HD.org) and may become an essential component for robust, automated, high‐throughput processing of MRI neuroimaging data.
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M3T: three-dimensional Medical image classifier using Multi-plane and Multi-slice Transformer
Jang, J., & Hwang, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02006 · Cited by 133
Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84
Omega-3 fatty acids are associated with blood-brain barrier integrity in a healthy aging population
Barnes, S., Chowdhury, S., Gatto, N. M., Fraser, G. E., & Lee, G. J. (2021) · Brain and Behavior · doi:10.1002/brb3.2273 · Cited by 47
Lee, W.-K., Wu, C.-C., Lee, C.-C., Lu, C.-F., Yang, H.-C., Huang, T.-H., Lin, C.-Y., Chung, W.-Y., Wang, P.-S., Wu, H.-M., et al. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101911 · Cited by 35
2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data
Ottesen, J. A., Yi, D., Tong, E., Iv, M., Latysheva, A., Saxhaug, C., Jacobsen, K. D., Helland, Å., Emblem, K. E., Rubin, D. L., et al. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.1056068 · Cited by 32
Metadata
- Authors
- Isensee, F., Schell, M., Pflueger, I., Brugnara, G., Bonekamp, D., Neuberger, U., Wick, A., Schlemmer, H., Heiland, S., Wick, W., Bendszus, M., Maier‐Hein, K. H., & Kickingereder, P.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2019-08-12
- Volume
- 40
- Issue
- 17
- Pages
- 4952-4964
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- OpenAlex
- W2968446587
- Cited by
- 722
- References
- 61
- FWCI
- 16.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Isensee, F., Schell, M., Pflueger, I., Brugnara, G., Bonekamp, D., Neuberger, U., Wick, A., Schlemmer, H., Heiland, S., Wick, W., Bendszus, M., Maier‐Hein, K. H., & Kickingereder, P. (2019). Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping. https://doi.org/10.1002/hbm.24750
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