Abstract
Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor volume (GTV). However, the large-scale variation in tumor volume is intractable, and the performance of current models is mostly unsatisfactory with indistinguishable and blurred boundaries of segmentation results of tiny tumor volume. To address the problem, we propose a densely connected deep convolutional network consisting of an encoder network and a corresponding decoder network, which extracts high-level semantic features from different levels and uses low-level spatial features concurrently to obtain fine-grained segmented masks. Skip-connection architecture is involved and modified to propagate spatial information to the decoder network. Preliminary experiments are conducted on 30 patients. Experimental results show our model outperforms all baseline models, with improvements of 4.17%. An ablation study is performed, and the effectiveness of the novel loss function is validated.
Highlights
support vector machine (SVM) can learn the actual distribution of magnetic resonance (MR) image data without prior knowledge, and segmentations are performed by a trained SVM classifier
The models are mainly evaluated according to Dice Similarity Coefficient (DSC)
A densely connected deep convolutional encoder–decoder network is proposed for Nasopharyngeal Carcinoma segmentation in MR slices
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Nasopharyngeal Carcinoma (NPC) is a type of malignant tumor with high incidence in East and Southeast Asia. In 2018, about 129,000 cases were reported, and more than
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