Publications and Software

Please find below a list of our recent publications and publicly available software. Our team is also invested in open science. Please check our Calgary-Campinas initiative for more details.


  1. Lopes, A., Souza, R., Pedrini, H., 2022. A Survey on RGB-D Datasets, Computer Vision and Image Understanding, Elsevier CVIU (accepted).

  2. Beauferris, Y., Karkalousos, D., Moriakov, N., Teuwen J., Caan, M., Rodrigues, L., Lopes, A., Pedrini H., Rittner, L., Dannecker, M., Studenyak, V., Gröger, F., Vyas, D., Faghih-Roohi, S., Jethi, A. K., Raju, J. C., Sivaprakasam, M., Lasby, M., Loos, W., Frayne R., Souza R., 2021. Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations, Frontiers in Neuroscience (accepted).

  3. Bento, M., Fantini, I, Park, J., Rittner, L., Frayne, R. 2022. Deep Learning in Large and Heterogeneous. Brain MR Imaging Datasets. Frontiers on Neuroinformatics: Special Issue - Multi-Site Neuroimage Analysis: Domain Adaptation and Batch Effects, 15:805669, pp. 1-17.

  4. Abyane, A. E., Zhu, D., Souza, R., Ma, L., Hemmati, H., 2022. Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness, Empirical Software Engineering (under review).

  5. Saat, P. Nogovitsyn, N., Souza, R., Hemmati, H., 2021. A Domain Adaptation Benchmark for T1-weighted Brain Magnetic Resonance Image Segmentation, NeuroImage (under review).

  6. Nogovitsyn, N., Metzak, P., Casseb, R., Souza, R., Harris, J., Prati, L., Zamyadi, M., Bray, S., Lebel, C., Hassel, S., Strother, S., Goldstein, B., Wang, J., Kennedy, S., MacQueen, G., Addington, J., 2022. Cerebello-limbic functional connectivity patterns in youth at clinical high risk for psychosis, Schizophrenia Research, 240, pp. 220-227.

  7. Herrera, W. J., Bento, M., Appenzeller, S., Reis, F., Pereira, D. R., Rittner, L., 2022. Automated Quality Check of Corpus Callosum Segmentation using Deep Learning. SPIE Medical Imaging.

  8. Fantini, I, Yasuda, C., Bento, M., Rittner, L., Cendes, F., Lotufo, R. 2021. Automatic MR image Quality Evaluation using a DeepCNN: A Reference-Free Method to Rate Motion Artifacts in Neuroimaging. Computerized Medical Imaging and Graphics, 90: 101897.

  9. Loos, W.S., Souza, R., Andersen, L.B., Lebel, R.M. and Frayne, R., 2021. Extraction of a vascular function for a fully automated dynamic contrast‐enhanced magnetic resonance brain image processing pipeline. Magnetic Resonance in Medicine.

  10. Souza, R., Beauferris, Y., Loos, W., Lebel, R.M. and Frayne, R., 2020. 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 Journal of Selected Topics in Signal Processing, 14(6), pp.1126-1136.

  11. Souza, R., Bento, M., Nogovitsyn, N., Chung, K.J., Loos, W., Lebel, R.M. and Frayne, R., 2020. Dual-domain cascade of U-nets for multi-channel magnetic resonance image reconstruction. Magnetic resonance imaging, 71, pp.140-153.

  12. Chung, K.J., Souza, R. and Frayne, R., 2019. Restoration of lossy JPEG-compressed brain MR images using cross-domain neural networks. IEEE Signal Processing Letters, 27, pp.141-145.

  13. Nogovitsyn, N., Souza, R., Muller, M., Srajer, A., Metzak, P.D., Hassel, S., Ismail, Z., Protzner, A., Bray, S.L., Lebel, C. and MacIntosh, B.J., 2020. Aberrant limbic brain structures in young individuals at risk for mental illness. Psychiatry and clinical neurosciences, 74(5), pp.294-302.

  14. Nogovitsyn, N., Muller, M., Souza, R., Hassel, S., Arnott, S.R., Davis, A.D., Hall, G.B., Harris, J.K., Zamyadi, M., Metzak, P.D. and Ismail, Z., 2020. Hippocampal tail volume as a predictive biomarker of antidepressant treatment outcomes in patients with major depressive disorder: a CAN-BIND report. Neuropsychopharmacology, 45(2), pp.283-291.

  15. Lucena, O., Souza, R., Rittner, L., Frayne, R. and Lotufo, R., 2019. Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks. Artificial intelligence in medicine, 98, pp.48-58.

  16. Bento, M., Souza, R., Salluzzi, M., Rittner, L., Zhang, Y. and Frayne, R., 2019. Automatic identification of atherosclerosis subjects in a heterogeneous MR brain imaging data set. Magnetic resonance imaging, 62, pp.18-27.

  17. Nogovitsyn, N., Souza, R., Muller, M., Srajer, A., Hassel, S., Arnott, S.R., Davis, A.D., Hall, G.B., Harris, J.K., Zamyadi, M. and Metzak, P.D., 2019. Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants. NeuroImage, 197, pp.589-597.

  18. Ghamisi, P., Maggiori, E., Li, S., Souza, R., Tarablaka, Y., Moser, G., De Giorgi, A., Fang, L., Chen, Y., Chi, M. and Serpico, S.B., 2018. New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation, sparse representation, and deep learning. IEEE geoscience and remote sensing magazine, 6(3), pp.10-43.

  19. Souza, R., Lucena, O., Garrafa, J., Gobbi, D., Saluzzi, M., Appenzeller, S., Rittner, L., Frayne, R. and Lotufo, R., 2018. An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement. NeuroImage, 170, pp.482-494.

  20. Souza, R., Rittner, L., Machado, R. and Lotufo, R., 2017. iamxt: Max-tree toolbox for image processing and analysis. SoftwareX, 6, pp.81-84.

  21. Júnior, P.R.M., Souza, R., Werneck, R.D.O., Stein, B.V., Pazinato, D.V., de Almeida, W.R., Penatti, O.A., Torres, R.D.S. and Rocha, A., 2017. Nearest neighbors distance ratio open-set classifier. Machine Learning, 106(3), pp.359-386.

  22. Ghamisi, P., Souza, R., Benediktsson, J.A., Rittner, L., Lotufo, R. and Zhu, X.X., 2016. Hyperspectral data classification using extended extinction profiles. IEEE Geoscience and Remote Sensing Letters, 13(11), pp.1641-1645.

  23. Ghamisi, P., Souza, R., Benediktsson, J.A., Zhu, X.X., Rittner, L. and Lotufo, R.A., 2016. Extinction profiles for the classification of remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 54(10), pp.5631-5645.

  24. Souza, R., Rittner, L. and Lotufo, R., 2014. A comparison between k-optimum path forest and k-nearest neighbors supervised classifiers. Pattern recognition letters, 39, pp.2-10.