A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation (2107.12435v1)

Abstract: Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify regions in the colon overlooked by the physicians during endoscopic examinations, and help detect and characterize lesions. In previous work, we have proposed the ResUNet++ architecture and demonstrated that it produces more efficient results compared with its counterparts U-Net and ResUNet. In this paper, we demonstrate that further improvements to the overall prediction performance of the ResUNet++ architecture can be achieved by using conditional random field and test-time augmentation. We have performed extensive evaluations and validated the improvements using six publicly available datasets: Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB, ASU-Mayo Clinic Colonoscopy Video Database, and CVC-VideoClinicDB. Moreover, we compare our proposed architecture and resulting model with other State-of-the-art methods. To explore the generalization capability of ResUNet++ on different publicly available polyp datasets, so that it could be used in a real-world setting, we performed an extensive cross-dataset evaluation. The experimental results show that applying CRF and TTA improves the performance on various polyp segmentation datasets both on the same dataset and cross-dataset.

• The paper demonstrates that ResUNet++ enhanced with CRF and TTA significantly improves the segmentation accuracy of colorectal polyps.
• The study validates the method using metrics like Dice coefficient and mIoU across six varied public datasets.
• The integration of these techniques promises better clinical applications by reducing missed detections in colonoscopy procedures.

Colorectal Polyp Segmentation Utilizing ResUNet++ with CRF and TTA

The paper presents an advanced method for the automated segmentation of colorectal polyps using a novel deep-learning architecture known as ResUNet++. The approach is reinforced with Conditional Random Fields (CRF) and Test-Time Augmentation (TTA) to enhance segmentation performance further. This comprehensive paper explores the efficacy of ResUNet++ on various publicly available datasets, aiming to improve the real-world applicability of Computer-Aided Diagnosis (CADx) systems in clinical settings.

Technical Overview

Colorectal cancer (CRC) remains a significant health issue worldwide, with colonoscopy being the gold standard for detection. However, traditional examination methods are limited by high miss-rates, necessitating the integration of AI-driven solutions to improve diagnostic accuracy and reduce oversight. ResUNet++, an encoder-decoder network based on U-Net and enhanced by residual blocks and attention mechanisms, is designed explicitly for medical image segmentation tasks.

Key enhancements include squeeze-and-excitation blocks to improve feature map recalibration, Atrous Spatial Pyramidal Pooling (ASPP) for capturing multi-scale contextual information, and attention units to emphasize relevant sections of input data. The paper validates the ResUNet++ architecture across six diverse polyp segmentation datasets, illustrating its robust performance and generalization capabilities.

Experimental Results

The application of CRF and TTA techniques has demonstrated measurable improvements in prediction performance, notably concerning challenging datasets with varying polyp shapes, sizes, and appearances. The paper deploys metrics such as Dice coefficient (DSC), mean Intersection over Union (mIoU), precision, recall, and ROC curve analysis to quantify segmentation accuracy.

The model performed exceptionally well on smaller and sessile polyps “often missed” during conventional colonoscopy. The incorporation of CRF and TTA has shown competitive results, with ResUNet++ plus TTA outperforming others in cross-dataset evaluations on single images while the combination with CRF shows strength in video datasets.

Implications and Future Work

The paper's findings have significant implications for the automated detection and diagnosis of CRC. By focusing on flat and sessile polyps, ResUNet++ demonstrated its potential in clinical applications, where standard detection approaches frequently fall short. The paper emphasizes the importance of developing generalizable AI models that can operate across different hospitals and interventions, a necessity for achieving practical clinical implementations.

The future direction suggested by this research involves enhancing generalization capabilities through multi-center trials and exploring prospective studies to assess real-world performance further. The integration of ResUNet++ into clinical workflows could reduce high miss-rates of polyps, particularly those less visible during standard examinations.

Conclusion

Overall, the paper successfully expands upon prior advancements in AI-driven biomedical image segmentation, presenting a robust and efficient architecture capable of addressing nuanced challenges within colorectal polyp detection. The proposed ResUNet++ model, bolstered by CRF and TTA, contributes to a significant stride toward realizing practical, automated diagnosis systems capable of operating with precision and reliability in diverse clinical environments.