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

Abstract

Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

Evolutionary route of nasopharyngeal carcinoma metastasis and its clinical significance

“…A total of 192 radiomics features were extracted for each sliding window. The radiomics features included three types of features, namely, statistical, texture and Gabor features [94][95][96] . (1) Statistical features: the grey value of the central point, momentum with order 1 to 5, was used.…”

Prognostic potential of a voxelwise invasion risk map of nasopharyngeal carcinoma based on a coordinate system of the nasopharynx

“…We assessed the automatic localization method of anatomical landmarks in MRI head images and achieved good results (51). There are many reports on the automatic segmentation of tumor ROIs for NPC in medical images (52)(53)(54)(55). Thus, using these above-mentioned techniques can automatically delineate the anatomical landmarks and tumor ROI, expand the sample size, and improve the accuracy of TIR.…”