MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy

TL;DR

This paper introduces MNet-SAt, a novel deep learning framework that enhances polyp segmentation in colonoscopy images by integrating multi-scale features and spatial attention, leading to superior accuracy and boundary preservation.

Contribution

The paper proposes a new multiscale network with spatial-enhanced attention modules specifically designed for precise polyp segmentation, addressing limitations of existing methods.

Findings

Achieved Dice scores of 96.61% on Kvasir-SEG and 98.60% on CVC-ClinicDB datasets.

Outperformed existing methods in boundary accuracy and generalization.

Demonstrated potential for clinical application in early polyp detection.

Abstract

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.