Below see a list of publications from our team related to our dataset:
Roberto Souza, Oeslle Lucena, Julia Garrafa, David Gobbi, Marina Saluzzi, Simone Appenzeller, Letícia Rittner, Richard Frayne, and Roberto Lotufo. "An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement." NeuroImage(2017). - Main publication
Oeslle Lucena, Roberto Souza, Leticia Rittner, Richard Frayne, and Roberto Lotufo. "Silver standard masks for data augmentation applied to deep-learning-based skull-stripping." In Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, pp. 1114-1117. IEEE, 2018.
Roberto Souza, Oeslle Lucena, Mariana Bento, Julia Garrafa, Simone Appenzeller, Leticia Rittner, Roberto Lotufo, and Richard Frayne. "Reliability of using single specialist annotation for designing and evaluating automatic segmentation methods: A skull stripping case study." In Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, pp. 1344-1347. IEEE, 2018.
Mariana Bento, Roberto Souza, Marina Salluzzi, and Richard Frayne. "Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol." In Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, p. 1095008. International Society for Optics and Photonics, 2019.
Mariana Bento, Roberto Souza, Richard Frayne. "Multicenter imaging studies: Automated approach to evaluating data variability and the role of outliers". In 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (pp. 182-188). IEEE.
Roberto Souza, Marc Lebel and Richard Frayne. "A Hybrid, Dual Domain, Cascade of Convolutional Neural Networks for Magnetic Resonance Image Reconstruction", Proceedings of the 2nd International Conference on Medical Imaging with Deep Learning.
Oeslle Lucena, Roberto Souza, Letícia Rittner, Richard Frayne and Roberto Lotufo. "Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Silver Standard Masks", Artificial Intelligence in Medicine (2019).
Roberto Souza, Richard Frayne. "A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction”, SIBGRAPI, 2019.
Roberto Souza, Oeslle Lucena, Mariana Bento, Julia Garrafa, Leticia Rittner, Simone Appenzeller (UNICAMP), Roberto Lotufo, Richard Frayne. "Brain Extraction Network Trained with `Silver Standard’ Data and Fine-tuned with Manual Annotation for Improved Segmentation”, SIBGRAPI, 2019.
Kevin J. Chung, Roberto Souza, and Richard Frayne, "Restoration of Lossy JPEG-compressed Brain MR Images using Cross-domain Neural Networks". IEEE Signal Processing Letters, 2019.
Roberto Souza, Mariana Bento , Nikita Nogovitsyn, Kevin J. Chung, Wallace Loos, R. Marc Lebel, Richard Frayne. "Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction", Elsevier Magnetic Resonance Imaging (ACCEPTED).
Roberto Souza, Youssef Beauferris, Wallace Loos, R. Marc Lebel and Richard Frayne. "Enhanced Deep-learning-based Magnetic Resonance Image Reconstruction by Leveraging Prior Subject-specific Brain Imaging: Proof-of-concept using a Cohort of Presumed Normal Subjects", IEEE JSTSP, 2020 (ACCEPTED).
Public deep-learning-based brain MR skull-stripping tool: CONSNet: Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver Standard Masks . In our opinion, it is currently one of the most accurate and fastest skull-stripping techniques for segmenting older normal subjects.