Keywords: Segmentation, Machine Learning/Artificial Intelligence
Accurate segmentation of nasopharyngeal tumor lesions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) facilitates subsequent diagnosis and treatment. However, current segmentation methods do not incorporate the pathological properties of the tumor. Therefore, this paper proposes a multimodal DCE-MRI segmentation method that uses the pharmacokinetic features of NPC, Ktrans, as modal information to assist nasopharyngeal tumor segmentation. We validated our method in several classical deep learning segmentation networks, and DCE-MRI with fused Ktrans eigenmodes had higher Dice coefficients than DCE-MRI with a single modality. The best segmentation results were obtained by this method on the ResUNet model (dice=74.39).
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