Publications

Clinical Validation is in our DNA.

AI must be tried and tested in real-world conditions, and our work with eminent research groups, radiology departments, and clinical experts ensures that our solutions perform accurately and robustly even under strenuous operating conditions like multiple OEMs, field strength, resolution, quality, and imaging artefacts.

Some of the work involving Pixyl technology includes :

Pauline Roca, Arnaud Attyé, Lucie Colas, Alan Tucholka, Pascal Rubini, Stenzel Cackowski, Juliette Ding, Jean-François Budzik, Felix Renard, Senan Doyle, Emmanuel L. Barbier, Imad Bousaid, Romain Casey, Sandra Vukusic, Nathalie Lassau, Sébastien Verclytte, and François Cotton. “Artificial intelligence to predict clinical disability status scale score in patients with multiple sclerosis using FLAIR MRI” Diagnostic and Interventional Imaging 2020.

Arnaud Attyé, Stenzel Cackowski, Alan Tucholka, Pauline Roca, Pascal Rubini, Sebastien Verclytte, Lucie Colas, Juliette Ding, Jean-François Budzik, Felix Renard, Emmanuel L Barbier, Romain Casey, Sandra Vukusic, and François Cotton. “Breaking the Clinico-Radiological Paradox in Multiple Sclerosis Using Machine Learning.” In Proceedings of the 28th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), Sydney, Australia, 2020.

Méreaux, Jean-Loup, Benjamin Hebant, Romain Lefaucheur, David Maltête, Nicolas Magne, Emmanuel Gérardin, and Bertrand Bourre. 2020. “Comparaison d’une analyse automatisée d’IRM par Pixyl versus imagerie de fusion dans le suivi de la sclérose en plaques.” In Journées des Jeunes Neurologues (J2N) et de la Recherche clinique, 2020.

Bonnan, M., Money, P., Desblache, P., Tucholka, A., Doyle, S., & Barroso, B. (2019). Atrophie corticale focale après une poussée méningée dans une sclérose en plaques progressive. Revue Neurologique, 175, S91.

Capet, N., Joly, H., Suply, C., Bresch, S., Mondot, L., Cohen, M. and Lebrun-Frénay, C., 2019. L’alexithymie dans la Sclérose en Plaques est associée à une atrophie de la substance blanche et des structures de substance grise centrales. Revue Neurologique, 175, p.S89.

Malaureille, Lison. “Comparison of automatic segmentation versus visual assessment of white matter hyperintensities and parenchymal atrophy in Alzheimer’s disease.” Thèse (2018): 37.

Commowick, Olivier, et al. “Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure.” Nature Scientific reports 8.1 (2018): 13650.

Alexander, Robert W. “Use of PIXYL software analysis of brain MRI (with & without contrast) as valuable metric in clinical trial tracking in study of multiple sclerosis (MS) and related neurodegenerative processes.” Clinical Trials in Degenerative Diseases 2.1 (2017): 1.

Alexander Robert W. “Use of Software Analytics of Brain MRI (with & without contrast) As Objective Metric in Neurological Disorders and Degenerative Diseases” 2017, International Physical Medicine & Rehabilitation Journal

Doyle, Senan, et al. “Sub-acute and Chronic Ischemic Stroke Lesion MRI Segmentation.” International MICCAI Brainlesion Workshop. Springer, Cham, 2017.

Maggia, Christophe, et al. “Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes.” International MICCAI Brainlesion Workshop. Springer, Cham, 2017.

Maggia, Christophe, et al. “Assessment of tissue injury in severe brain trauma.” BrainLes 2015. Springer, Cham, 2015.

Maggia, Christophe, et al. “Traumatic Brain Lesion Quantification based on Mean Diffusivity Changes.” International MICCAI Brainlesion Workshop. Springer, Cham, 2017.  

Thomas Mistral et al. “Automatic Quantification of Brain Lesion Volume from MR Images after Severe Traumatic Brain Injury” for radiology

Menze, Bjoern H., et al. “The multimodal brain tumor image segmentation benchmark (BRATS).” IEEE transactions on medical imaging 34.10 (2014): 1993-2024.

Doyle, S., et al. “Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM.” Procs. NCI-MICCAI BraTS (2013): 18-22.

Forbes, Florence, et al. “Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation.” 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. IEEE, 2010.

Forbes, Florence, et al. “A weighted multi-sequence markov model for brain lesion segmentation.” Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 2010.