Abstract:
Abstract: In recent years， the incidence of gastric cancer in China has been continuously rising， while the diagnosis rate of early gastric cancer remains relatively low. As an important means for diagnosing early gastric cancer， magnifying endoscopy can observe micro lesions， but traditional diagnostic methods are difficult to quantitatively analyze， which limits its application in clinical practice and poses a great challenge to the treatment and prognosis of patients. In order to assist in the diagnosis of early gastric cancer， improve the survival rate and prognosis of patients， the 3D reconstruction algorithm of magnified gastroscopy images based on deep learning has become a research hotspot. This paper proposes to use the InstantMesh framework， combined with a multi-view diffusion model and a sparse view reconstruction model， and crops images based on the coordinate information of the lesion area segmented in the previous single magnified gastroscopy image， achieves the construction of a high-quality 3D mesh model for single lesion area images. This method not only improves the reconstruction accuracy， reduces noise interference， but also makes the lesion features clearer. Experimental results show that this method is significantly better than the existing state-of-the-art single-view 3D reconstruction algorithms such as Unique3D， TripoSR， Convolutional Reconstruction Model （CRM） and One-2-3-45 in both qualitative and quantitative evaluation of medical image 3D reconstruction. This study aims to provide strong technical support for early diagnosis and treatment of gastric cancer， makes substantial contributions to improve the prevention and treatment of gastric cancer in my country.

Key words: Key words: deep learning, early gastric cancer, 3D reconstruction, InstantMesh, multi-view diffusion model, sparse view reconstruction model