Publications

International Journals

• Moreau, N., Rousseau, C., Fourcade, C., Santini, G., Brennan, A., Ferrer, L., Lacombe M., Guillerminet C., Colombié M., Jézéquel P., Campone M., Normand N., & Rubeaux, M. Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment. Cancers, 2021, vol.14, no 1, p.101. https://doi.org/10.3390/cancers14010101. PDF
  JCR: 6.575 (2021), 4.5 (2023)
  SCIMAGO: Oncologie - Q1

• Fourcade, C., Ferrer, L., Moreau, N., Santini, G., Brennan, A. Rousseau, C., Lacombe, M., Fleury, V., Colombié, M., Jézéquel, P., Campone, M., Rubeaux, M., Mateus, D. Deformable Image Registration with Deep Network Priors: a Study on Longitudinal PET Images. Physics in Medicine and Biology, 2022, vol.67, no 15, p. 155011. https://doi.org/10.1088/1361-6560/ac7e17. PDF
  JCR: 3.5 (2022), 3.3 (2023)
  SCIMAGO: Radiologie, Nuclear Medicine and Imaging - Q1

International Conferences

• Moreau, N., Shabani, M., Schell, C., Bozek, K. GlomNet: A HoVer Deep Learning model for glomerulus instance segmentation. In : 2024 IEEE 21th International Symposium on Biomedical Imaging (ISBI). IEEE, 2024, p. 1-5. https://doi.org/10.1109/ISBI56570.2024.10635729. PDF
  ERA: A, Qualis: B1

• Moreau, N., Rousseau, C., Fourcade, C., Ferrer, L., Lacombe, M., Guillerminet C., Colombié, M., Campone, M., Jézéquel, P., Rubeaux, M., & Normand, N. Can deep learning predict the receptors’ status of breast cancer’s metastases on PET/CT images? In : Annual Congress of the European Association of Nuclear Medicine October 15-19, 2022 Barcelona, Spain., Eur J Nucl Med Mol Imaging, 2022, 49 (Suppl 1), EP-460, p. S620-S621. https://doi.org/10.1007/s00259-022-05924-4. PDF

• Moreau, N., Rousseau, C., Fourcade, C., Santini, G., Ferrer, L., Lacombe, M., Guillerminet C., Campone, M., Colombié, M., Rubeaux, M., & Normand, N. Influence of inputs for bone lesion segmentation in longitudinal 18F-FDG PET/CT imaging studies. In : 2022 44nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2022, p.4736-4739. https://doi.org/10.1109/EMBC48229.2022.9871081. PDF
  ERA: A, Qualis: B1

• Fourcade, C., Frenel, J-S., Moreau, N., Santini, G., Brennan, A., Rousseau, C., Lacombe, M., Fleury, V., Colombié, M., Jézéquel, P., Maucherat, B., Campone, M., Mateus, D., Ferrer, L., & Rubeaux, M. PERCIST-like response assessment with FDG PET based on automatic segmentation of all lesions in metastatic breast cancer. In : American Society of Clinical Oncology (ASCO) Annual Meeting, Journal of Clinical Oncology, 2022, vol. 40. http://dx.doi.org/10.1200/JCO.2022.40.16_suppl.e13057. PDF

• Moreau, N., Rousseau, C., Fourcade, C., Santini, G., Ferrer, L., Lacombe, M., Guillerminet, C., Jézéquel, P., Campone, M., Normand, N., & Rubeaux, M. Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images. In : Medical Imaging 2021: Computer-Aided Diagnosis, International Society for Optics and Photonics, 2021, vol. 11597, p. 115972U. https://doi.org/10.1117/12.2580892. PDF

• Santini, G., Obame, Y. N., Fourcade, C., Moreau, N., & Rubeaux, M. Automatic classification of benign and malignant kidney masses using radiomics. A retrospective study exploiting the KiTS19 dataset. In : Medical Imaging 2021: Image Processing, International Society for Optics and Photonics, 2021, Vol. 11596, p. 115962K. https://doi.org/10.1117/12.2579901.

• Santini, G., Fourcade, C., Moreau, N., Rousseau, C., Ferrer, L., Lacombe, M., Fleury, V., Campone M., Jézéquel, P. & Rubeaux, M. Unpaired PET/CT image synthesis of liver region using CycleGAN. In 16th International Symposium on Medical Information Processing and Analysis, International Society for Optics and Photonics, 2020, vol. 11583, p. 115830T. https://doi.org/10.1117/12.2576095.

• Moreau, N., Rousseau, C., Fourcade, C., Santini, G., Ferrer, L., Lacombe, M., Guillerminet C., Campone, M., Colombié, M., Rubeaux, M., & Normand, N. Deep learning approaches for bone and bone lesion segmentation on 18F-FDG PET/CT imaging in the context of metastatic breast cancer. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020, p. 1532-1535. https://doi.org/10.1109/EMBC44109.2020.9175904. PDF
  ERA: A, Qualis: B1

• Fourcade, C., Ferrer, L., Santini, G., Moreau, N., Rousseau, C., Lacombe, M., Guillerminet, C., Colombié, M., Campone, M., Mateus, D., & Rubeaux, M. Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images. In : 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, 2020, p. 1536-1539. https://doi.org/10.1109/embc44109.2020.9175683. PDF
  ERA: A, Qualis: B1

National Conferences

• Santini, G., Moreau, N., Fourcade, C., Rousseau, C., Ferrer, L., Campone, M., Colombié, M., Jézéquel, P., & Rubeaux, M. Quantification automatique de l’activité de fond pour le calcul du critère PERCIST. Médecine Nucléaire, 2021. https://doi.org/10.1016/j.mednuc.2021.06.080. PDF

• Moreau, N., Rousseau, C., Ferrer, L., Campone, M., Colombié, M., Normand, N., & Rubeaux, M. Comparison between traditional and deep learning-based semi-automatic segmentation methods for metastatic breast cancer lesions monitoring. In NTHS-Nuclear Technology for Health Symposium, 2020. https://nths2020.sciencesconf.org/data/pages/book_of_abstracts_NTHS_2020.pdf. PDF

Others

• Santini, G., Moreau, N., & Rubeaux, M. Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge. arXiv preprint, 2019. https://arxiv.org/pdf/1909.00735.pdf. PDF

• Moreau, N. Deep learning methods to segment and characterize PET/CT images in the context of metastatic breast cancer. Nantes University, 2022. https://theses.hal.science/tel-04232357.