Polyp segmentation in medical imaging: challenges, approaches and future directions

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 17, 2025

Язык: Английский

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Synergistic Multi-Granularity Rough Attention UNet for Polyp Segmentation

Journal of Imaging, Год журнала: 2025, Номер 11(4), С. 92 - 92

Опубликована: Март 21, 2025

Automatic polyp segmentation in colonoscopic images is crucial for the early detection and treatment of colorectal cancer. However, complex backgrounds, diverse morphologies, ambiguous boundaries make this task difficult. To address these issues, we propose Synergistic Multi-Granularity Rough Attention U-Net (S-MGRAUNet), which integrates three key modules: Hybrid Filtering (MGHF) module extracting multi-scale contextual information, Dynamic Granularity Partition Synergy (DGPS) enhancing polyp-background differentiation through adaptive feature interaction, (MGRA) mechanism further optimizing boundary recognition. Extensive experiments on ColonDB CVC-300 datasets demonstrate that S-MGRAUNet significantly outperforms existing methods while achieving competitive results Kvasir-SEG ClinicDB datasets, validating its accuracy, robustness, generalization capability, all effectively reducing computational complexity. This study highlights value multi-granularity extraction attention mechanisms, providing new insights practical guidance advancing theories medical image segmentation.

Язык: Английский

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A survey of deep learning algorithms for colorectal polyp segmentation

Neurocomputing, Год журнала: 2024, Номер 614, С. 128767 - 128767

Опубликована: Окт. 30, 2024

Язык: Английский

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Dataset-level color augmentation and multi-scale exploration methods for polyp segmentation

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125395 - 125395

Опубликована: Сен. 1, 2024

Язык: Английский

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BFE-Net: bilateral fusion enhanced network for gastrointestinal polyp segmentation

Biomedical Optics Express, Год журнала: 2024, Номер 15(5), С. 2977 - 2977

Опубликована: Март 22, 2024

Accurate segmentation of polyp regions in gastrointestinal endoscopic images is pivotal for diagnosis and treatment. Despite advancements, challenges persist, like accurately segmenting small polyps maintaining accuracy when resemble surrounding tissues. Recent studies show the effectiveness pyramid vision transformer (PVT) capturing global context, yet it may lack detailed information. Conversely, U-Net excels semantic extraction. Hence, we propose bilateral fusion enhanced network (BFE-Net) to address these challenges. Our model integrates PVT features via a deep feature enhancement module (FEF) attention decoder (AD). Experimental results demonstrate significant improvements, validating our model's across various datasets modalities, promising advancements

Язык: Английский

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A local–global unified scheme driven by positionable texture and multi-level boundary for lung cancer organoids segmentation

Expert Systems with Applications, Год журнала: 2024, Номер 250, С. 123888 - 123888

Опубликована: Апрель 4, 2024

Язык: Английский

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Dataset-Level Color Augmentation and Multi-Scale Exploration Methods for Polyp Segmentation

Опубликована: Янв. 1, 2024

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Язык: Английский

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