Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.
Charley 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 Assunta RoccaMassimo FilippiRohit BakshiShahamat TauhidFerran PradosMarios C. YiannakasHugh KearneyOlga CiccarelliSeth A. SmithConstantina Andrada TreabaCaterina MaineroJennifer LefeuvreDaniel S. ReichGovind NairVincent AuclairDonald G. McLarenAllan R. MartinMichael G. FehlingsShahabeddin VahdatAli KhatibiJulien DoyonTimothy M. ShepherdErik CharlsonSridar NarayananJulien Cohen-AdadPublished in: CoRR (2018)
Keyphrases
- spinal cord
- convolutional neural networks
- automatic segmentation
- multiple sclerosis lesions
- automatically segmenting
- magnetic resonance images
- manual segmentation
- x ray images
- medical images
- segmentation algorithm
- semi automatic segmentation
- mr images
- accurate segmentation
- brain tissue
- medical imaging
- mri data
- magnetic resonance imaging
- brain structures
- white matter
- medical image analysis
- x ray
- high resolution
- brain tumors
- computer vision
- quantitative analysis
- image segmentation