Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.
Leonardo RundoLucian BeerStephan UrsprungPaula Martin-GonzalezFlorian MarkowetzJames D. BrentonMireia Crispin-OrtuzarEvis SalaRamona WoitekPublished in: Comput. Biol. Medicine (2020)
Keyphrases
- ct images
- medical images
- medical imaging
- computed tomography
- soft tissue
- pet ct
- lung nodules
- liver segmentation
- lymph nodes
- lung parenchyma
- ct scans
- computer tomography
- ct imaging
- fracture detection
- unsupervised fuzzy clustering
- anatomical structures
- image analysis
- medical image segmentation
- anatomical knowledge
- dce mri
- pet images
- medical image analysis
- magnetic resonance images
- computer aided diagnosis
- region of interest
- mr images
- partial volume
- image segmentation
- treatment planning
- ct data
- imaging modalities
- fully automatic
- cancer diagnosis
- accurate segmentation
- x ray images
- magnetic resonance imaging
- magnetic resonance
- segmentation algorithm
- computer vision
- medical image processing
- pulmonary nodules
- brain tumors
- x ray
- level set