Edge-aware Feature Aggregation Network for Polyp Segmentation

Precise polyp segmentation is vital for the early diagnosis and prevention of colorectal cancer (CRC) in clinical practice. However, due to scale variation and blurry polyp boundaries, it is still a challenging task to achieve satisfactory segmentation performance with different scales and shapes. In this study, we present a novel Edge-aware Feature Aggregation Network (EFA-Net) for polyp segmentation, which can fully make use of cross-level and multi-scale features to enhance the performance of polyp segmentation. Specifically, we first present an Edge-aware Guidance Module (EGM) to combine the low-level features with the high-level features to learn an edge-enhanced feature, which is incorporated into each decoder unit using a layer-by-layer strategy. Besides, a Scale-aware Convolution Module (SCM) is proposed to learn scale-aware features by using dilated convolutions with different ratios, in order to effectively deal with scale variation. Further, a Cross-level Fusion Module (CFM) is proposed to effectively integrate the cross-level features, which can exploit the local and global contextual information. Finally, the outputs of CFMs are adaptively weighted by using the learned edge-aware feature, which are then used to produce multiple side-out segmentation maps. Experimental results on five widely adopted colonoscopy datasets show that our EFA-Net outperforms state-of-the-art polyp segmentation methods in terms of generalization and effectiveness.Comment: 20 pages 8 figure

Clinical evaluation of radiation-induced sinusitis by MRI-based scoring system in nasopharyngeal carcinoma patients

Abstract Objective To explore the application of magnetic resonance imaging (MRI) in the evaluation of radiation-induced sinusitis (RIS), MRI-based scoring system was used to evaluate the development regularity, characteristics and influencing factors of RIS in nasopharyngeal carcinoma (NPC) patients. Patients and methods A retrospective analysis was performed by collecting the clinical and MRI data of 346 NPC patients to analyze the characteristics and prognosis of RIS. The predictive model was constructed according to the influencing factors of RIS. Results (1) In the 2-year follow-up after radiotherapy (RT), there was significant change in L-M score in both groups of NPC patients (sinusitis before RT group: p = 0.000 vs. non-sinusitis before RT group: p = 0.000). After 6 months of RT, the L-M scores of the two groups tended to plateau (sinusitis before RT group: p = 0.311 vs. non-sinusitis before RT group: p = 0.469). (2) The prevalence of sinusitis in two groups of NPC patients (without or with sinusitis before RT) was 83% vs. 93%, 91% vs. 99%, 94% vs. 98% at 1, 6 and 24 months after RT, respectively. (3) In the patients without sinusitis before RT, the incidence of sinusitis in maxillary and anterior/posterior ethmoid, sphenoid and frontal sinuses was 87.1%, 90.0%/87.1%, 49.5%, 11.8% respectively, 1 month after RT. (4) A regression model was established according to the univariate and multivariate analysis of the factors related to RIS (smoking history: p = 0.000, time after RT: p = 0.008 and TNM staging: p = 0.040). Conclusion (1) RIS is a common complication in NPC patients after RT. This disorder progressed within 6 months after RT, stabilized and persisted within 6 months to 2 years. There is a high incidence of maxillary sinus and ethmoid sinus inflammation in NPC patients after RT. (2) Smoking history, time after RT and TNM staging were significant independent risk factors for RIS. (3) The intervention of the risk factors in the model may prevent or reduce the occurrence of RIS in NPC patients