The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy
Open accessAbstract
Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image markers after scan acquisition. We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in 20 s. To enable training of the complex network with millions of learnable parameters using limited annotated data, we propose to first pre-train on auxiliary labels created from existing segmentation software. Subsequently, the pre-trained model is fine-tuned on manual labels to rectify errors in auxiliary labels. With this learning strategy, we are able to use large neuroimaging repositories without manual annotations for training. In an extensive set of evaluations on eight datasets that cover a wide age range, pathology, and different scanners, we demonstrate that QuickNAT achieves superior segmentation accuracy and reliability in comparison to state-of-the-art methods, while being orders of magnitude faster. The speed up facilitates processing of large data repositories and supports translation of imaging biomarkers by making them available within seconds for fast clinical decision making.
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Fully convolutional networks for semantic segmentation
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
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Cited by
The Medical Segmentation Decathlon
Antonelli, M., Reinke, A., Bakas, S., Farahani, K., Kopp-Schneider, A., Landman, B. A., Litjens, G., Menze, B., Ronneberger, O., Summers, R. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30695-9 · Cited by 1,384
Self-citationConcurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks
Roy, A. G., Navab, N., & Wachinger, C. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00928-1_48 · Cited by 869
Self-citationBrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. (2019) · arXiv · doi:10.48550/arxiv.1905.06731 · Cited by 212
Self-citationAssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation
Coupé, P., Mansencal, B., Clément, M., Giraud, R., Denis de Senneville, B., Ta, V.-T., Lepetit, V., & Manjon, J. V. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117026 · Cited by 206
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images
Kim, B. N., Dolz, J., Jodoin, P.-M., & Desrosiers, C. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3065727 · Cited by 43
Metadata
- Authors
- Guha Roy, A., Conjeti, S., Navab, N., & Wachinger, C.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 186
- Pages
- 713-727
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2901434856
- Cited by
- 267
- References
- 53
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
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