Colorectal cancer is one of the three most common cancers worldwide. Early detection and assessment of polyps can significantly reduce the risk of developing colorectal cancer. Physicians can obtain information about polyp regions through polyp segmentation techniques, enabling the provision of targeted treatment plans. This study systematically reviews polyp segmentation methods. We investigated 146 papers published between 2018 and 2024 and conducted an in-depth analysis of the methodologies employed. Based on the selected literature, we systematically organized this review. First, we analyzed the development and evolution of the polyp segmentation field. Second, we provided a comprehensive overview of deep learning-based polyp image segmentation methods and the Mamba method, as well as video polyp segmentation methods categorized by network architecture, addressing the challenges faced in polyp segmentation. Subsequently, we evaluated the performance of 44 models, including segmentation performance metrics and real-time analysis capabilities. Additionally, we introduced commonly used datasets for polyp images and videos, along with metrics for assessing segmentation models. Finally, we discussed existing issues and potential future trends in this area.

Colorectal cancer is the third most commonly diagnosed cancer worldwide, and early detection of polyps via colonoscopy is essential for improving patient survival. However, automatic polyp segmentation faces three key challenges: balancing global context with local detail, delineating ambiguous boundaries under low contrast, and handling large variations in polyp size and morphology. To address these challenges, we propose WMA-Net, a Wavelet-Guided Mamba-Attention Network that uses wavelet-domain semantic–boundary separation as the organizing design principle. Rather than introducing a new individual operator, the contribution lies in how existing components—wavelet decomposition, Mamba state space modeling, multi-directional pixel difference convolution, and uncertainty-aware reverse attention—are combined and coordinated within one boundary-aware framework. The architecture integrates pixel difference convolution for multi-directional edge detection, frequency-selective cross-scale fusion with dual-stream wavelet-domain processing, Mamba-based multi-scale aggregation with linear complexity, and uncertainty-aware progressive boundary refinement. Extensive experiments on five public polyp benchmarks demonstrate state-of-the-art performance on four out of five datasets. On the seen datasets, WMA-Net achieves mean Dice scores of 94.4% on CVC-ClinicDB and 93.6% on Kvasir-SEG. On the unseen datasets, WMA-Net attains 91.7% on CVC-300, 82.3% on CVC-ColonDB, and 83.8% on ETIS-LaribPolypDB, demonstrating robust cross-dataset generalization. Comprehensive ablation studies validate the effectiveness and synergy of each proposed module.

Biomass-templated spinel ferrite/carbon nanocomposites are promising electrode materials; however, dense carbon shells formed during low-temperature carbonization can block electrolyte access to active sites and impair electrochemical performance. The role of this carbon-shell passivation in biomass-derived ferrite/carbon composites remains largely unexplored. Here, we identify this limitation in a stepwise-synthesized NiFe₂O₄/rGO@C composite (NFC-S, BET surface area = 5.23 m² g⁻¹, ΔEp = 113.3 mV) and resolve it through a rationally designed one-pot optimization strategy in which urea simultaneously serves as a pore-forming agent and nitrogen precursor. The optimized composite (N-NFC-O) achieves a BET surface area of 186.39 m² g⁻¹—a 35.6-fold enhancement—with 70.1% micropore contribution and 3.13 at.% in-situ nitrogen doping. Electrochemically, ΔEp narrows to 72.3 mV and enables efficient NO₂⁻ oxidation with a ~70 mV cathodic shift, whereas NFC-S shows negligible catalytic response under identical conditions. As a proof of concept, differential pulse voltammetry (DPV) yields a nitrite sensitivity of 9.17 µA cm⁻² mM⁻¹ and a detection limit of 92.5 µM. Overall, this work identifies carbon pore accessibility as a key structural descriptor governing electrocatalytic performance in biomass-derived ferrite/carbon composites and provides a general design strategy for developing high-performance biomass-derived carbon/oxide hybrid electrodes.