Polyp segmentation in colonoscopy images using DeepLabV3++

TL;DR

This paper introduces DeepLabV3++, an enhanced deep learning model for polyp segmentation in colonoscopy images, achieving high accuracy and robustness, outperforming existing models, and aiding early colorectal cancer detection.

Contribution

The study presents DeepLabV3++, a novel architecture with improved modules for multi-scale feature capture, outperforming previous models in polyp segmentation accuracy.

Findings

Achieved Dice scores over 96% on three datasets.

Outperformed several state-of-the-art models.

Significantly reduced segmentation errors.

Abstract

Segmenting polyps in colonoscopy images is essential for the early identification and diagnosis of colorectal cancer, a significant cause of worldwide cancer deaths. Prior deep learning based models such as Attention based variation, UNet variations and Transformer-derived networks have had notable success in capturing intricate features and complex polyp shapes. In this study, we have introduced the DeepLabv3++ model which is an enhanced version of the DeepLabv3+ architecture. It is designed to improve the precision and robustness of polyp segmentation in colonoscopy images. We have utilized The proposed model incorporates diverse separable convolutional layers and attention mechanisms within the MSPP block, enhancing its capacity to capture multi-scale and directional features. Additionally, the redesigned decoder further transforms the extracted features from the encoder into a more meaningful segmentation map. Our model was evaluated on three public datasets (CVC-ColonDB, CVC-ClinicDB, Kvasir-SEG) achieving Dice coefficient scores of 96.20%, 96.54%, and 96.08%, respectively. The experimental analysis shows that DeepLabV3++ outperforms several state-of-the-art models in polyp segmentation tasks. Furthermore, compared to the baseline DeepLabV3+ model, our DeepLabV3++ with its MSPP module and redesigned decoder architecture, significantly reduced segmentation errors (e.g., false positives/negatives) across small, medium, and large polyps. This improvement in polyp delineation is crucial for accurate clinical decision-making in colonoscopy.