Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

16 May 2018Charley GrosBenjamin De LeenerAtef BadjiJosefina MaranzanoDominique EdenSara M. DupontJason TalbottRen ZhuoquiongYaou LiuTobias GranbergRussell OuelletteYasuhiko TachibanaMasaaki HoriKouhei KamiyaLydia ChougarLeszek StawiarzJan HillertElise BannierAnne KerbratGilles EdanPierre LabaugeVirginie CallotJean PelletierBertrand AudoinHenitsoa RasoanandrianinaJean-Christophe BrissetPaola ValsasinaMaria A. RoccaMassimo FilippiRohit BakshiShahamat TauhidFerran PradosMarios YiannakasHugh KearneyOlga CiccarelliSeth SmithConstantina Andrada TreabaCaterina MaineroJennifer LefeuvreDaniel S. ReichGovind NairVincent AuclairDonald G. McLarenAllan R. MartinMichael G. FehlingsShahabeddin VahdatAli KhatibiJulien DoyonTimothy ShepherdErik CharlsonSridar NarayananJulien Cohen-Adad

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines... (read more)

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