| Autor: |
Raghaw, Chandravardhan Singh, Yadav, Aryan, Sanjotra, Jasmer Singh, Dangi, Shalini, Kumar, Nagendra |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Biomedical Signal Processing and Control Biomedical Signal Processing and Control, Volume 102, April 2025, 107363 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1016/j.bspc.2024.107363 |
| Popis: |
Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitations of current approaches in preserving precise polyp boundaries, incorporating multi-scale features, and modeling spatial dependencies that accurately reflect the intricate and diverse morphology of polyps. Methods: To address these limitations, we propose a novel Multiscale Network with Spatial-enhanced Attention (MNet-SAt) for polyp segmentation in colonoscopy images. This framework incorporates four key modules: Edge-Guided Feature Enrichment (EGFE) preserves edge information for improved boundary quality; Multi-Scale Feature Aggregator (MSFA) extracts and aggregates multi-scale features across channel spatial dimensions, focusing on salient regions; Spatial-Enhanced Attention (SEAt) captures spatial-aware global dependencies within the multi-scale aggregated features, emphasizing the region of interest; and Channel-Enhanced Atrous Spatial Pyramid Pooling (CE-ASPP) resamples and recalibrates attentive features across scales. Results: We evaluated MNet-SAt on the Kvasir-SEG and CVC-ClinicDB datasets, achieving Dice Similarity Coefficients of 96.61% and 98.60%, respectively. Conclusion: Both quantitative (DSC) and qualitative assessments highlight MNet-SAt's superior performance and generalization capabilities compared to existing methods. Significance: MNet-SAt's high accuracy in polyp segmentation holds promise for improving clinical workflows in early polyp detection and more effective treatment, contributing to reduced colorectal cancer mortality rates. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
