Nasopharyngeal carcinoma (NPC) is a malignant tumor whose accurate segmentation is a prerequisite for patient treatment and prognosis. Location and area of lymph nodes (LN) provide significant information for patient staging. However, existing methods for segmenting primary NPC tumors and LNs ignore three major challenges, i.e., uncertain position, irregular boundary, and false negative, thus, obtaining unsatisfactory segmentation results. Given the remarkable success of capturing global context information in Transformer, we have tailored a Transformer-based architecture, named NPCTrans, to address these limitations. NPCTrans is underpinned by three critical modules, including the position locating module (PLM), the boundary attention module (BAM), and the lesion region correction module (LRC). The PLM aims to improve sensitivity towards lesion regions by utilizing relative position encodings and control position information by gated in global attention. The BAM captures and refines the key points of the irregular boundary to enhance boundary segmentation performance. The LRC focuses on multi-scale feature learning by augmenting features of pixels and regions of interest at different scales to correctly identify multiple lesion regions to reduce false negatives. Extensive benchmarking experiments are conducted on our benchmarking dataset, which includes 9,100 samples collected from 723 patients, and results demonstrate that NPCTrans outperforms existing state-of-the-art models.

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients’ survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features’ estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods.

Nasopharyngeal carcinoma (NPC) is a malignant tumor whose survivability is greatly improved if early diagnosis and timely treatment are provided. Accurate segmentation of both the primary NPC tumors and metastatic lymph nodes (MLNs) is crucial for patient staging and radiotherapy scheduling. However, existing studies mainly focus on the segmentation of primary tumors, eliding the recognition of MLNs, and thus fail to comprehensively provide a landscape for tumor identification. There are three main challenges in segmenting primary NPC tumors and MLNs: variable location, variable size, and irregular boundary. To address these challenges, we propose an automatic segmentation network, named by NPCNet, to achieve segmentation of primary NPC tumors and MLNs simultaneously. Specifically, we design three modules, including position enhancement module (PEM), scale enhancement module (SEM), and boundary enhancement module (BEM), to address the above challenges. First, the PEM enhances the feature representations of the most suspicious regions. Subsequently, the SEM captures multiscale context information and target context information. Finally, the BEM rectifies the unreliable predictions in the segmentation mask. To that end, extensive experiments are conducted on our dataset of 9124 samples collected from 754 patients. Empirical results demonstrate that each module realizes its designed functionalities and is complementary to the others. By incorporating the three proposed modules together, our model achieves state-of-the-art performance compared with nine popular models.

Accurate and reliable segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance image (MRI) is important for treatment planning and follow-up evaluation. However, it is still challenging because NPC is infiltrative with a vague border and has a tiny volume with varying sizes and shapes. The above problems easily cause the NPC tumors’ features to submerge in the process of feature extraction. Standard segmentation methods do not cope with the “feature submergence” and performed unsatisfactorily in NPC segmentation. To address the problem, a dual-supervised method equipped with the detection-and-excitation module (DEM) was proposed. DEM strengthens the region of interest (ROI) and weakens complex and immense background through incorporating detection feature maps (outline maps) with intermediated segmentation features. To be specific, the DEM explored the outline of the ROI and output a detection feature map by using a supervised layer guided by a tailored loss function. Then, the DEM uses the results to recalibrates the intermediated segmentation. Finally, a neural network equipped with the proposed DEM was elaborately designed to achieve accurate segmentation. We applied the proposed network termed as DENet on a real nasopharyngeal carcinoma dataset to realize automatic NPC tumors segmentation in multi-modality MR images. The proposed DEM enables the neural network to segment NPC tumors within the immense and complex background. Experimental results have demonstrated the effectiveness of the proposed method by comparing it with benchmark models.

Wide-band interference (WBI) is a critical issue for synthetic aperture radar (SAR), which may severely affect the imaging quality of SAR systems. To suppress WBI effectively, a novel interference suppression algorithm based on robust principal component analysis (RPCA) in time-frequency-pulse (TF-P) domain is proposed. For SAR echoes in TF-P domain, there are two useful properties: 1) The TF characteristic of useful signal in adjacent pulse are similar, indicating that useful signal has low-rank property; 2) Due to its variation of position and sparsely distrusted in TF-P domain, WBI has sparse characteristic. According to these properties, RPCA method is applied to decompose the TF-P matrix into a low-rank matrix (i.e. useful signal) and a sparse matrix (i.e. WBI). Finally, the WBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.