Augmented diagnosis, quantification, characterization, image reconstruction: medical artificial intelligence at your fingertips for radiology


AI solution to support diagnosis and monitoring of multiple sclerosis (MS)

Pixyl.Neuro.MS is a deep learning tool assisting radiologists in MRI diagnosis and monitoring of multiple sclerosis (MS) patients. For each region of the brain, Pixyl.Neuro.MS counts the lesions present and calculates their overall volume, integrating the results fully automatically in the radiologist’s usual reading environment. The algorithm studies T2 FLAIR or T1 GADO sequences to identify hypersignals caused by white matter inflammation in MS.

Automatic detection, quantification and characterization of lesions in anatomical areas of interest

Pixyl.Neuro.MS measures the volume and lesion load for the entire brain and for every brain regions of interest (periventricular, juxtacortical, infratentorial, deep white matter) with regard to McDonald’s criteria (2017 revision)).

Longitudinal analysis of disease evolution if prior exam is present in the PACS

Pixyl.Neuro.MS automatically measures disease activity by segmenting and characterizing white matter evolution of lesions based on prior exams.

Automated production of a structured report and a segmented series (lesion outline) directly in the PACS

Pixyl.neuro provides the radiologist with a complete quantitative report on the lesions, their characteristics and evolution, as well as their visualization on the annotated brain images.

  • multiple sclerosis
  • T2 FLAIR
  • T1 GADO
  • lesion load
  • MRI
  • detection
  • longitudinal analysis

Save time


Save time thanks to a complete automation of certain time-consuming tasks such as counting lesions or comparing current exam with prior exams.

Increase precision


Decrease variability of intra- and inter-operator measurements, and standardize follow-ups for your patients.

Integrate in your infrastructure


Integrate AI support tools directly into your existing infrastructure and into your regular clinical workflow

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La haute sensibilité de l’analyse Pixyl peut permettre de détecter précocement des changements dans l’activité de la maladie, conduisant à une meilleure prise en charge du patient.

Dr Arnaud Attye,

CHU Grenoble, France


  1. Use of Software Analytics of Brain MRI (with & without contrast) As Objective Metric in Neurological Disorders and Degenerative Diseases. Int Phys Med Rehab J 2017, 2(2): 00046
  2. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). Menze et al. DOI 10.1109/TMI.2014.2377694
  3. Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure. Nature Scientific Reports | (2018) 8:13650 | DOI:10.1038/s41598-018-31911-7

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