MAUNet: Polyp segmentation network based on multiscale feature fusion of attention U‐shaped network structure DOI

Jianwu Long,

Jian Lin,

Jiayin Liu

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2024, Volume and Issue: 34(3)

Published: May 1, 2024

Abstract Colorectal cancer is a prevalent malignant tumor affecting the digestive tract. Although colonoscopy remains most effective method for colon examination, it may occasionally fail to detect polyps. In an effort enhance detection rate of intestinal polyps during colonoscopy, we propose MAUNet, polyp segmentation network based on multi‐scale feature fusion Attention U‐shaped structure. Our model incorporates advanced components, including Receptive Field Block, Reverse and Residual Refinement Module, mirroring analytical process performed by medical imaging professionals. We evaluated performance MAUNet five challenging datasets conducted comparative analysis against state‐of‐the‐art models using six evaluation metrics. The experimental results demonstrate that achieves varying levels improvement across datasets. Particularly Kvasir dataset, Mean Dice IOU metrics reached 91.6% 84.3%, respectively, confirming model's outstanding in segmentation.

Language: Английский

A frequency attention-embedded network for polyp segmentation DOI Creative Commons
Rui Tang,

Hejing Zhao,

Yao Tong

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 10, 2025

Gastrointestinal polyps are observed and treated under endoscopy, so there presents significant challenges to advance endoscopy imaging segmentation of polyps. Current methodologies often falter in distinguishing complex polyp structures within diverse (mucosal) tissue environments. In this paper, we propose the Frequency Attention-Embedded Network (FAENet), a novel approach leveraging frequency-based attention mechanisms enhance accuracy significantly. FAENet ingeniously segregates processes image data into high low-frequency components, enabling precise delineation boundaries internal by integrating intra-component cross-component mechanisms. This method not only preserves essential edge details but also refines learned representation attentively, ensuring robust across varied conditions. Comprehensive evaluations on two public datasets, Kvasir-SEG CVC-ClinicDB, demonstrate FAENet's superiority over several state-of-the-art models terms Dice coefficient, Intersection Union (IoU), sensitivity, specificity. The results affirm that advanced significantly improve quality, outperforming traditional contemporary techniques. success indicates its potential revolutionize clinical practices, fostering diagnosis efficient treatment gastrointestinal

Language: Английский

Citations

1
Label-Decoupled Medical Image Segmentation With Spatial-Channel Graph Convolution and Dual Attention Enhancement DOI
Qingting Jiang, Hailiang Ye, Bing Yang

et al.

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2024, Volume and Issue: 28(5), P. 2830 - 2841

Published: Feb. 20, 2024

Deep learning-based methods have been widely used in medical image segmentation recently. However, existing works are usually difficult to simultaneously capture global long-range information from images and topological correlations among feature maps. Further, often suffer blurred target edges. Accordingly, this paper proposes a novel framework named label-decoupled network with spatial-channel graph convolution dual attention enhancement mechanism (LADENet for short). It constructs learnable adjacency matrices utilizes convolutions effectively on spatial locations dependencies between different channels an image. Then strategy based distance transformation is introduced decouple original label into body edge supervising the branch branch. Again, mechanism, designing block branch, built promote learning ability of region boundary features. Besides, interactor devised fully consider interaction branches improve performance. Experiments benchmark datasets reveal superiority LADENet compared state-of-the-art approaches.

Language: Английский

Citations

5
Multi-view orientational attention network combining point-based affinity for polyp segmentation DOI
Yan Liu, Yan Yang, Yongquan Jiang

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 249, P. 123663 - 123663

Published: March 19, 2024

Language: Английский

Citations

4
InCoLoTransNet: An Involution-Convolution and Locality Attention-Aware Transformer for Precise Colorectal Polyp Segmentation in GI Images DOI
Yassine Oukdach, Anass Garbaz,

Zakaria Kerkaou

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 17, 2025

Gastrointestinal (GI) disease examination presents significant challenges to doctors due the intricate structure of human digestive system. Colonoscopy and wireless capsule endoscopy are most commonly used tools for GI examination. However, large amount data generated by these technologies requires expertise intervention identification, making manual analysis a very time-consuming task. Thus, development computer-assisted system is highly desirable assist clinical professionals in decisions low-cost effective way. In this paper, we introduce novel framework called InCoLoTransNet, designed polyp segmentation. The study based on transformer convolution-involution neural network, following encoder-decoder architecture. We employed vision encoder section focus global context, while decoder involves collaboration resampling features. Involution enhances model's ability adaptively capture spatial contextual information, convolution focuses local leading more accurate feature extraction. essential features captured passed through two skip connection pathways. CBAM module refines passes them block, leveraging attention mechanisms emphasize relevant information. Meanwhile, locality self-attention pass involution reinforcing regions. Experiments were conducted five public datasets: CVC-ClinicDB, CVC-ColonDB, Kvasir-SEG, Etis-LaribPolypDB, CVC-300. results obtained InCoLoTransNet optimal when compared with 15 state-of-the-art methods segmentation, achieving highest mean dice score 93% CVC-ColonDB 90% intersection over union, outperforming methods. Additionally, distinguishes itself terms segmentation generalization performance. It achieved high scores coefficient union unseen datasets as follows: 85% 79% 91% 87% CVC-300, 70% respectively.

Language: Английский

Citations

0
RootEx: An automated method for barley root system extraction and evaluation DOI Creative Commons
Maichol Dadi, Alessandra Lumini, Annalisa Franco

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 230, P. 110030 - 110030

Published: Feb. 6, 2025

Language: Английский

Citations

0
MFAR-Net: Multi-level feature interaction and Dual-Dimension adaptive reinforcement network for breast lesion segmentation in ultrasound images DOI
Guoqi Liu,

Sun Dong,

Yanan Zhou

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126727 - 126727

Published: Feb. 1, 2025

Language: Английский

Citations

0
SCABNet: A Novel Polyp Segmentation Network With Spatial‐Gradient Attention and Channel Prioritization DOI Creative Commons
Khaled ELKarazle, Valliappan Raman,

Caslon Chua

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(2)

Published: Feb. 6, 2025

ABSTRACT Current colorectal polyps detection methods often struggle with efficiency and boundary precision, especially when dealing of complex shapes sizes. Traditional techniques may fail to precisely define the boundaries these polyps, leading suboptimal rates. Furthermore, flat small blend into background due their low contrast against mucosal wall, making them even more challenging detect. To address challenges, we introduce SCABNet, a novel deep learning architecture for efficient polyps. SCABNet employs an encoder‐decoder structure three blocks: Feature Enhancement Block (FEB), Channel Prioritization (CPB), Spatial‐Gradient Boundary Attention (SGBAB). The FEB applies dilation spatial attention high‐level features, enhancing discriminative power improving model's ability capture patterns. CPB, alternative traditional channel blocks, assigns prioritization weights diverse feature channels. SGBAB replaces conventional mechanisms solution that focuses on map. It Jacobian‐based approach construct learned convolutions both vertical horizontal components This allows effectively understand changes in map across different locations, which is crucial detecting complex‐shaped These blocks are strategically embedded within network's skip connections, capabilities without imposing excessive computational demands. They exploit enhance features at levels: high, mid, low, thereby ensuring wide range has been trained Kvasir‐SEG CVC‐ClinicDB datasets evaluated multiple datasets, demonstrating superior results. code available on: https://github.com/KhaledELKarazle97/SCABNet .

Language: Английский

Citations

0
Dynamic Frequency-Decoupled Refinement Network for Polyp Segmentation DOI Creative Commons
Yao Tong,

Jyh‐Wen Chai,

Ziqi Chen

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 277 - 277

Published: March 11, 2025

Polyp segmentation is crucial for early colorectal cancer detection, but accurately delineating polyps challenging due to their variations in size, shape, and texture low contrast with surrounding tissues. Existing methods often rely solely on spatial-domain processing, which struggles separate high-frequency features (edges, textures) from low-frequency ones (global structures), leading suboptimal performance. We propose the Dynamic Frequency-Decoupled Refinement Network (DFDRNet), a novel framework that integrates frequency-domain processing. DFDRNet introduces Frequency Adaptive Decoupling (FAD) module, dynamically separates high- components, (FAR) refines these components before fusing them spatial enhance accuracy. Embedded within U-shaped encoder–decoder framework, achieves state-of-the-art performance across three benchmark datasets, demonstrating superior robustness efficiency. Our extensive evaluations ablation studies confirm effectiveness of balancing accuracy computational

Language: Английский

Citations

0
MSPolypNet: A residual multi-scale semantic approach for polyps segmentation DOI
Shreerudra Pratik, Pallabi Sharma, Bunil Kumar Balabantaray

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110224 - 110224

Published: March 11, 2025

Language: Английский

Citations

0
Polyp segmentation in medical imaging: challenges, approaches and future directions DOI Creative Commons
Abdul Qayoom, Juanying Xie, Haider Ali

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(6)

Published: March 17, 2025

Language: Английский

Citations

0