Within colorectal cancer diagnostics, conventional colonoscopy techniques face critical limitations, including a limited field of view and a lack of depth information, which can impede the detection of precancerous lesions. Current methods struggle to provide comprehensive and accurate 3D reconstructions of the colonic surface which can help minimize the missing regions and reinspection for pre-cancerous polyps. Addressing this, we introduce 'Gaussian Pancakes', a method that leverages 3D Gaussian Splatting (3D GS) combined with a Recurrent Neural Network-based Simultaneous Localization and Mapping (RNNSLAM) system. By introducing geometric and depth regularization into the 3D GS framework, our approach ensures more accurate alignment of Gaussians with the colon surface, resulting in smoother 3D reconstructions with novel viewing of detailed textures and structures. Evaluations across three diverse datasets show that Gaussian Pancakes enhances novel view synthesis quality, surpassing current leading methods with a 18% boost in PSNR and a 16% improvement in SSIM. It also delivers over 100X faster rendering and more than 10X shorter training times, making it a practical tool for real-time applications. Hence, this holds promise for achieving clinical translation for better detection and diagnosis of colorectal cancer.

翻译：在结直肠癌诊断中，传统结肠镜检查技术存在视野有限和缺乏深度信息等关键限制，可能阻碍癌前病变的检测。现有方法难以提供结肠表面的全面准确三维重建，而这种重建有助于减少未覆盖区域并对癌前息肉进行二次检查。针对这一问题，我们提出"高斯薄饼"方法，该方法结合三维高斯泼溅技术与基于循环神经网络的同步定位与地图构建系统。通过在三维高斯泼溅框架中引入几何与深度正则化，我们的方法能够实现高斯体与结肠表面更精确的对齐，从而生成更平滑的三维重建结果，并呈现出精细纹理与结构的新颖视角。在三个不同数据集上的评估表明，高斯薄饼方法提升了新视角合成质量，PSNR提升18%、SSIM提升16%，显著超越当前主流方法。同时，该方法实现了超过100倍的渲染加速和10倍以上的训练时间缩短，使其成为实时应用的实用工具。因此，该方法有望实现临床转化，以改善结直肠癌的检测与诊断效果。