Visual analysis still represents the gold-standard for CT image interpretation, conveying crucial information regarding the diagnosis and prognosis of lung cancer. This work presents the first automatic approach to quantify and classify the lung tumour heterogeneity based on dynamic contrast enhanced-CT (DCE-CT) image sequences, so as it is performed through visual analysis by expert.
Baiocco, S., Barone, D., Gavelli, G., Bevilacqua, A. (2016). Visual-like classification of lung tumour heterogeneity in DCE-CT sequences.
Visual-like classification of lung tumour heterogeneity in DCE-CT sequences
BAIOCCO, SERENA;GAVELLI, GIAMPAOLO;BEVILACQUA, ALESSANDRO
2016
Abstract
Visual analysis still represents the gold-standard for CT image interpretation, conveying crucial information regarding the diagnosis and prognosis of lung cancer. This work presents the first automatic approach to quantify and classify the lung tumour heterogeneity based on dynamic contrast enhanced-CT (DCE-CT) image sequences, so as it is performed through visual analysis by expert.File in questo prodotto:
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