Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model

TL;DR

Extensive experiments show superior segmentation performance of the proposed method against state-of-the-art polyp segmentation baselines across five polyp benchmarks in both superior learning and generalization capabilities.

Abstract

Automatic polyp segmentation is crucial for effective diagnosis and treatment in colonoscopy images. Traditional methods encounter significant challenges in accurately delineating polyps due to limitations in feature representation and the handling of variability in polyp appearance. Deep learning techniques, including CNN and Transformer-based methods, have been explored to improve polyp segmentation accuracy. However, existing approaches often neglect additional semantics, restricting their ability to acquire adequate contexts of polyps in colonoscopy images. In this paper, we propose an innovative method named ``Automatic Polyp Segmentation with Self-Enriched Semantic Model'' to address these limitations. First, we extract a sequence of features from an input image and decode high-level features to generate an initial segmentation mask. Using the proposed self-enriched semantic module, we query potential semantics and augment deep features with additional semantics, thereby aiding the model in understanding context more effectively. Extensive experiments show superior segmentation performance of the proposed method against state-of-the-art polyp segmentation baselines across five polyp benchmarks in both superior learning and generalization capabilities.

Figures (7)

• Figure 1: Deep learning-based automatic polyp segmentation methods often include encoder and decoder parts. Contemporary models struggle to identify and categorize challenging features highlighted within green-bordered areas. This region appears relatively blurry and distinct from the surrounding polyp objects leading to confusion between normal tissues and actual polyps, thereby causing segmentation failures. Providing supplementary semantics promotes the model to obtain comprehensive contextual information about polyp objects, leading to a greatly segmentation performance.
• Figure 2: Overview of our architecture. The proposed method consists of an Encoder (Section \ref{['sec:encoder']}), a Decoder (Section \ref{['sec:decoder']}) and a Self-Enriched Semantic (Section \ref{['sec:semantic']}) module. The Encoder extracts a sequence of multi-scale features from an input image. The Decoder aggregates high-level features to generate an initial segmentation mask. The Self-Enriched Semantic provides supplementary semantics to high-level features to relocate polyp objects.
• Figure 3: Qualitative results with the current polyp segmentation baselines. Green indicates a predicted mask. It can be found, our proposed model can precisely recognize and segment polyp objects even under the variability in polyp appearance attached to noises, ambiguous boundaries, and intricate foregrounds.
• Figure 4: Qualitative results with the current polyp segmentation baselines. Green indicates a predicted mask. It can be found, our proposed model can precisely recognize and segment polyp objects even under the variability in polyp appearance attached to noises, ambiguous boundaries, and intricate foregrounds.
• Figure 5: Visualization of the ablation study results. As can be seen, removing Self-Enrich Semantic (SES) leads to segmentation failures in challenging semantic areas, whereas the removal of Local-to-Global Spatial Fusion (LGSF) causes incorrectly segmentation results denoted as red-bordered boxes.