ODC-SA Net: Orthogonal Direction Enhancement and Scale Aware Network for Polyp Segmentation

TL;DR

The paper introduces ODC-SA Net, a novel network for polyp segmentation that effectively captures multi-directional features and scale variations, improving accuracy in early colorectal cancer detection.

Contribution

It proposes the Orthogonal Direction Convolutional block and Multi-scale Fusion Attention mechanism to enhance feature extraction and scale awareness in polyp segmentation.

Findings

Outperforms state-of-the-art methods on public datasets.

Effectively captures multi-directional features and scale changes.

Improves edge and size detection accuracy.

Abstract

Accurate polyp segmentation is crucial for the early detection and prevention of colorectal cancer. However, the existing polyp detection methods sometimes ignore multi-directional features and drastic changes in scale. To address these challenges, we design an Orthogonal Direction Enhancement and Scale Aware Network (ODC-SA Net) for polyp segmentation. The Orthogonal Direction Convolutional (ODC) block can extract multi-directional features using transposed rectangular convolution kernels through forming an orthogonal feature vector basis, which solves the issue of random feature direction changes and reduces computational load. Additionally, the Multi-scale Fusion Attention (MSFA) mechanism is proposed to emphasize scale changes in both spatial and channel dimensions, enhancing the segmentation accuracy for polyps of varying sizes. Extraction with Re-attention Module (ERA) is used to re-combinane effective features, and Structures of Shallow Reverse Attention Mechanism (SRA) is used to enhance polyp edge with low level information. A large number of experiments conducted on public datasets have demonstrated that the performance of this model is superior to state-of-the-art methods.