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

Yang Li1, Guanghui Han2,3, Xiujian Liu2

  • 1School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China.

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|December 10, 2021
PubMed
Summary
This summary is machine-generated.

Accurate segmentation of Nasopharyngeal Carcinoma in MRI is crucial for radiotherapy. A novel deep convolutional network improves gross tumor volume delineation, outperforming existing models for precise cancer treatment planning.

Keywords:
DenseNetnasopharyngeal carcinoma segmentationsupervised learning

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Accurate segmentation of Nasopharyngeal Carcinoma (NPC) in Magnetic Resonance Imagery (MRI) is critical for effective radiotherapy planning.
  • Current segmentation models struggle with the large variability in tumor size and often produce blurred boundaries, especially for smaller tumors.

Purpose of the Study:

  • To develop an improved deep convolutional neural network for precise Nasopharyngeal Carcinoma segmentation in MRI.
  • To enhance the delineation of the gross tumor volume (GTV) for accurate radiotherapy dose delivery.

Main Methods:

  • A densely connected deep convolutional network with an encoder-decoder architecture was proposed.
  • The network utilizes skip-connections to integrate high-level semantic and low-level spatial features for fine-grained segmentation masks.
  • A novel loss function was developed and validated.

Main Results:

  • The proposed model demonstrated superior performance compared to baseline models in preliminary experiments on 30 patients.
  • An improvement of 4.17% was observed in segmentation accuracy.
  • An ablation study confirmed the effectiveness of the novel loss function.

Conclusions:

  • The developed densely connected deep convolutional network offers a promising solution for accurate Nasopharyngeal Carcinoma segmentation in MRI.
  • This advancement can lead to more precise radiotherapy planning and improved patient outcomes.