Colonoscopy polyp image segmentation method with boundary self-knowledge distillation

Abstract

Objective Colorectal cancer remains a formidable global health challenge，underscoring the pressing need for early detection strategies to improve treatment outcomes. Among these strategies，colonoscopy stands out as a primary diagnostic tool，relying on the visual acumen of medical professionals to identify potentially cancerous abnormalities，such as polyps，within the colon and rectum. However，the effectiveness of colonoscopy is heavily contingent upon the skill and experience of the operator，leading to variability and limitations in detection rates across different practitioners and settings. In response to these challenges，the integration of artificial intelligence and computer vision techniques has garnered increasing attention as a means to augment the accuracy and efficiency of colorectal cancer screening. Various algorithms have been developed to automatically segment colorectal images，with the overarching goal of precisely delineating polyps from the surrounding tissue. Despite advancements in this domain，many existing models confront inherent inefficiencies and limited effectiveness stemming from their intricate architectures and dependence on manual feature engineering. Method This study proposes a novel end-to-end boundary self-knowledge distillation（BA-KD）framework，which aims to achieve precise polyp segmentation. In contrast to conventional methods，BA-KD seamlessly integrates boundary and polyp segmentation networks into a unified framework，facilitating effective knowledge transfer between the two domains. BA-KD represents a pioneering contribution in this field，aiming to harness the synergistic benefits of both boundary and polyp information for increased segmentation accuracy. The BA-KD framework comprises two interconnected branches：a boundary segmentation network serving as the teacher branch and a polyp segmentation network acting as the student branch. The inherent challenges associated with delineating polyp boundaries are addressed by introducing a boundary detection operator to automatically generate boundary masks，which are subsequently leveraged when training both branches. This approach not only enhances the segmentation performance of the student branch but also enriches the knowledge base of the teacher branch，thereby fostering mutual learning and refinement. A key distinguishing feature of BA-KD is the shared image feature extractors between the student and teacher branches，facilitating robust knowledge transfer across both domains. Two innovative structures，namely，reverse multilevel feature fusion （RMLF） and reverse feature fusion （RFM），are proposed to facilitate the effective fusion of feature information at various hierarchical levels. RMLF enables the integration of high-level features to generate a comprehensive global feature map，whereas RFM synergistically combines reverse shallow features with high-level features aggregated via RMLF to produce the final segmentation mask. Result A comprehensive experimental validation of the BA-KD results is conducted against seven state-of-the-art methods across four different datasets：CVC-ClinicDB，CVC-ColonDB，Kvasir，and HAM10000. The comparative models include U-Net，Double-UNet，UNet++，TransFuse，PraNet，DuAT，RaBit，GroupSeg，and G-CASCADE. These models serve as benchmarks in the polyp segmentation and general medical image segmentation domains. The results on CVC-ClinicDB show that BA-KD demonstrates exceptional performance in terms of mSpe and mDSC，with values of 0. 997 and 0. 955 5，respectively. BA-KD outperforms all the competitors in terms of mDSC and mIoU，with improvements of 0. 45% and 0. 68%，respectively，over RaBit. For the CVC-ColonDB dataset，BA-KD outperforms all the other methods across all the evaluation metrics，achieving improvements of 2. 20% in the mIoU and 1. 51% in the mDSC compared with the optimal performance achieved by TransFuse. For the Kvasir dataset，BA-KD achieves an mIoU of 0. 889 and an mDSC of 0. 937，surpassing the best-performing RaBit by approximately 1. 08% and 1. 14%，respectively. Furthermore，the generalization ability of BA-KD is evaluated on other medical segmentation tasks via the HAM10000 dataset，which includes dermoscopic images from different populations. Compared with existing medical segmentation baselines，BA-KD excels in all metrics on HAM10000，achieving significant scores in mDSC（0. 956 2）and mIoU（0. 922 3），surpassing the best-performing Double UNet by 1. 45% and 2. 25%，respectively. Conclusion The experimental results clearly demonstrate that BA-KD outperforms existing state-of-the-art segmentation methods，with substantial improvements in the Dice similarity coefficient （DSC）and mean intersection over union metrics（mIoU）.