Prompt-based polyp segmentation during endoscopy.

Accurate judgment and identification of polyp size is crucial in endoscopic diagnosis. However, the indistinct boundaries of polyps lead to missegmentation and missed cancer diagnoses. In this paper, a prompt-based polyp segmentation method (PPSM) is proposed to assist in early-stage cancer diagnosis during endoscopy. It combines endoscopists' experience and artificial intelligence technology. Firstly, a prompt-based polyp segmentation network (PPSN) is presented, which contains the prompt encoding module (PEM), the feature extraction encoding module (FEEM), and the mask decoding module (MDM). The PEM encodes prompts to guide the FEEM for feature extracting and the MDM for mask generating. So that PPSN can segment polyps efficiently. Secondly, endoscopists' ocular attention data (gazes) are used as prompts, which can enhance PPSN's accuracy for segmenting polyps and obtain prompt data effectively in real-world. To reinforce the PPSN's stability, non-uniform dot matrix prompts are generated to compensate for frame loss during the eye-tracking. Moreover, a data augmentation method based on the segment anything model (SAM) is introduced to enrich the prompt dataset and improve the PPSN's adaptability. Experiments demonstrate the PPSM's accuracy and real-time capability. The results from cross-training and cross-testing on four datasets show the PPSM's generalization. Based on the research results, a disposable electronic endoscope with the real-time auxiliary diagnosis function for early cancer and an image processor have been developed. Part of the code and the method for generating the prompts dataset are available at https://github.com/XinZhenRen/PPSM.