DCNet: Densely Connected Deep Convolutional Encoder–Decoder Network for Nasopharyngeal Carcinoma Segmentation

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 mostl...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 21; číslo 23; s. 7877
Hlavní autoři: Li, Yang, Han, Guanghui, Liu, Xiujian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 26.11.2021
MDPI
Témata:
Cancer
Datasets
Deep learning
DenseNet
Machine learning
nasopharyngeal carcinoma segmentation
Radiation therapy
Semantics
supervised learning
Support vector machines
Throat cancer
Tumors
ISSN:1424-8220, 1424-8220
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Shrnutí: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.
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Co-first authors.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21237877