A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.
Vitoantonio BevilacquaAntonio BrunettiGiacomo Donato CascaranoAndrea GuerrieroFrancesco PesceMarco MoschettaLoreto GesualdoPublished in: BMC Medical Informatics Decis. Mak. (2019)
Keyphrases
- magnetic resonance images
- brain mri
- medical images
- multiple sclerosis
- brain scans
- learning frameworks
- partial volume
- brain tumors
- medical imaging
- tissue segmentation
- multiple sclerosis lesions
- brain structures
- bias field
- mr images
- magnetic resonance imaging
- white matter
- gray matter
- anatomical structures
- brain tissue
- partial volume effects
- segmentation algorithm
- medical image analysis
- mri data
- intensity distribution
- level set
- brain mr images
- corpus callosum
- high resolution
- diffusion tensor
- brain tumor segmentation
- image segmentation
- segmentation method
- computer vision
- image analysis
- accurate segmentation
- feature extraction
- high quality
- case study
- pairwise
- edge detection
- x ray
- semi supervised learning
- fully automatic