Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images

Abstract

Nasopharyngeal carcinoma (NPC) is prevalent in certain areas, such as South China, Southeast Asia, and the Middle East. Radiation therapy is the most efficient means to treat this malignant tumor. Positron emission tomography-computed tomography (PET-CT) is a suitable imaging technique to assess this disease. However, the large amount of data produced by numerous patients causes traditional manual delineation of tumor contour, a basic step for radiotherapy, to become time-consuming and labor-intensive. Thus, the demand for automatic and credible segmentation methods to alleviate the workload of radiologists is increasing. This paper presents a method that uses fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. This work is the first to segment NPC using dual-modality PET-CT images. This technique is identical to what is used in clinical practice and offers considerable convenience for subsequent radiotherapy. The deep supervision introduced by auxiliary paths can explicitly guide the training of lower layers, thus enabling these layers to learn more representative features and improve the discriminative capability of the model. Results of threefold cross-validation with a mean dice score of 87.47% demonstrate the efficiency and robustness of the proposed method. The method remarkably outperforms state-of-the-art methods in NPC segmentation. We also validated by experiments that the registration process among different subjects and the auxiliary paths strategy are considerably useful techniques for learning discriminative features and improving segmentation performance.

Automated delineation of nasopharynx gross tumor volume for nasopharyngeal carcinoma by plain CT combining contrast-enhanced CT using deep learning

“…Zhao et al presented a method that used 2D FCNN with dual-modality PET-CT images of 30 patients to achieve automated segmentation of NPC. The deep supervision adding auxiliary paths could explicitly direct the training of lower layers and learn more representative features to improve the performance of the model (Zhao et al, 2019). Ma et al investigated an enhanced CNN method for automated NPC segmentation using CT and MRI.…”

A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences

“…It shows theoretically that combining multiple views can obtain abundant information for latent intact space learning Multiview discriminant analysis with view consistency (MvDA-VC) [ 16 ]: it seeks for a single discriminant common space for multiple views in a nonpairwise manner by jointly learning multiple view-specific linear transforms. MvDA-VC method has achieved good performance in addressing the problem of object recognition from multiple views Zhao et al [ 12 ]: it uses fully convolutional networks with an auxiliary path to achieve automatic segmentation of NPC on dual-modality PET-CT images. The proposed method improves NPC segmentation by guiding the training of lower layers by auxiliary paths Li et al [ 13 ]: it proposes a modified version of the U-Net, which performs well on NPC segmentation by modifying the downsampling layers and upsampling layers to have a similar learning ability and predict the same spatial resolution as the source image …”

“…[ 11 ] reported an automatic NPC segmentation method based on the convolutional neural network (CNN) architecture with dynamic contrast-enhanced MRI. [ 12 ] used fully convolutional networks with auxiliary paths to achieve automatic segmentation of NPC on PET-CT images. [ 13 ] used a modified U-Net model to automatically segment NPC on CT images from 502 patients.…”

Artificial Intelligence in Nuclear Medicine

“…Before planned radiotherapy, it is necessary to precisely quantify the target structures by segmentation which, in the case of nasopharyngeal carcinomas, is often a particularly difficult and time-consuming activity because of the anatomic location. Zhao et al showed, for a small group of 30 patients, that the automatic segmentation of such tumors on 18 F-FDG PET/CT data was, in principle, possible using the U-Net architecture (mean Dice score of 87.47%) (44). Other groups applied similar approaches to head and neck cancer (45) and lung cancer (46,47).…”