Label-Decoupled Medical Image Segmentation With Spatial-Channel Graph Convolution and Dual Attention Enhancement

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

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Enhanced Colon Cancer Segmentation and Image Synthesis through Advanced Generative Adversarial Networks based-Sine Cosine Algorithm

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 105354 - 105369

Published: Jan. 1, 2024

Colorectal cancer (CRC) is a prevalent and life-threatening malignancy, demanding early diagnosis effective treatment for improved patient outcomes. Accurate segmentation of colon in medical images challenging task due to the complexity its morphology limited annotated data availability. This paper presents an efficient approach image synthesis, combining Attention U-Net Pix2Pix Generative Adversarial Network (Pix2Pix-GAN) guided by Sine Cosine Algorithm (SCA) hyperparameter tuning within GAN framework. The utilization SCA plays pivotal role optimizing delicate balance between generator discriminator dynamics, resulting enhanced convergence stability. Our method achieved state-of-the-art results with mean Dice score 0.9514, Intersection over Union 0.9123, F beta 0.9636, similarity index 0.9430 outperforming existing methods. Moreover, Mean Absolute Error reached minimal value 0.01583. proposed shows promise enhancing accuracy robustness which could lead better cancer.

Language: Английский

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The Devil Is in the Boundary: Boundary-enhanced Polyp Segmentation

IEEE Transactions on Circuits and Systems for Video Technology, Journal Year: 2024, Volume and Issue: 34(7), P. 5414 - 5423

Published: Jan. 1, 2024

Due to the various appearance of polyps and tiny contrast between polyp area its surrounding background, accurate segmentation has become a challenging task. To tackle this issue, we introduce boundary-enhanced framework for segmentation, called Focused on Boundary Segmentation (FoBS) framework, that leverages multi-level collaboration among sample, feature, optimization. It places greater emphasis boundary improve accuracy segmentation. Firstly, boundary-aware mixup method is designed model's awareness boundary. More importantly, propose deformable laplacian-based feature refining explicitly strengthen representation ability features. employs Laplacian refinement function capture discriminative information from perceptual field, thereby improving adapt variations. In addition, self-adjusting coefficient learning enables adaptive control over strength at each location. Furthermore, develop location-sensitive compensation criterion assigns more importance degraded after during Extensive quantitative qualitative experiments four benchmarks demonstrate effectiveness our automatic Our code available https://github.com/TFboys-lzz/ FoBS.

Language: Английский

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FEGNet: A Feedback Enhancement Gate Network for Automatic Polyp Segmentation

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2023, Volume and Issue: 27(7), P. 3420 - 3430

Published: May 1, 2023

Regular colonoscopy is an effective way to prevent colorectal cancer by detecting polyps. Automatic polyp segmentation significantly aids clinicians in precisely locating areas for further diagnosis. However, a challenge problem, since polyps appear variety of shapes, sizes and textures, they tend have ambiguous boundaries. In this paper, we propose U-shaped model named Feedback Enhancement Gate Network (FEGNet) accurate overcome these difficulties. Specifically, the high-level features, design novel Recurrent Module (RGM) based on feedback mechanism, which can refine attention maps without any additional parameters. RGM consists Feature Aggregation Attention (FAAG) Multi-Scale (MSM). FAAG aggregate context information, MSM applied capturing multi-scale critical task. addition, straightforward but edge extraction module detect boundaries low-level used guide training early features. our experiments, quantitative qualitative evaluations show that proposed FEGNet has achieved best results compared other state-of-the-art models five datasets.

Language: Английский

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An Edge-Enhanced Network for Polyp Segmentation

Bioengineering, Journal Year: 2024, Volume and Issue: 11(10), P. 959 - 959

Published: Sept. 25, 2024

Colorectal cancer remains a leading cause of cancer-related deaths worldwide, with early detection and removal polyps being critical in preventing disease progression. Automated polyp segmentation, particularly colonoscopy images, is challenging task due to the variability appearance low contrast between surrounding tissues. In this work, we propose an edge-enhanced network (EENet) designed address these challenges by integrating two novel modules: covariance attention (CEEA) cross-scale edge enhancement (CSEE) modules. The CEEA module leverages covariance-based enhance boundary detection, while CSEE bridges multi-scale features preserve fine-grained details. To further improve accuracy introduce hybrid loss function that combines cross-entropy edge-aware loss. Extensive experiments show EENet achieves Dice score 0.9208 IoU 0.8664 on Kvasir-SEG dataset, surpassing state-of-the-art models such as Polyp-PVT PraNet. Furthermore, it records 0.9316 0.8817 CVC-ClinicDB demonstrating its strong potential for clinical application segmentation. Ablation studies validate contribution

Language: Английский

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Dynamic Frequency-Decoupled Refinement Network for Polyp Segmentation

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

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Rethinking Polyp Segmentation from the Perspectives of Matching Views and Seeking Camouflage

Multimedia Systems, Journal Year: 2025, Volume and Issue: 31(3)

Published: May 7, 2025

Language: Английский

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)

Published: May 22, 2025

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Progressive Two-Stage Decoder Segment Anything Model for Polyp Segmentation

Published: Jan. 10, 2025

Language: Английский

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Contextual-SAM: Segment Anything Model with Contextual Representation for Polyp Segmentation

Published: Jan. 10, 2025

Language: Английский

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