EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images

Depth estimation is a foundational component for 3D reconstruction in minimally invasive endoscopic surgeries. However, existing monocular depth estimation techniques often exhibit limited performance to the varying illumination and complex textures of the surgical environment. While powerful visual foundation models offer a promising solution, their training on natural images leads to significant domain adaptability limitations and semantic perception deficiencies when applied to endoscopy. In this study, we introduce EndoUFM, an unsupervised monocular depth estimation framework that innovatively integrating dual foundation models for surgical scenes, which enhance the depth estimation performance by leveraging the powerful pre-learned priors. The framework features a novel adaptive fine-tuning strategy that incorporates Random Vector Low-Rank Adaptation (RVLoRA) to enhance model adaptability, and a Residual block based on Depthwise Separable Convolution (Res-DSC) to improve the capture of fine-grained local features. Furthermore, we design a mask-guided smoothness loss to enforce depth consistency within anatomical tissue structures. Extensive experiments on the SCARED, Hamlyn, SERV-CT, and EndoNeRF datasets confirm that our method achieves state-of-the-art performance while maintaining an efficient model size. This work contributes to augmenting surgeons' spatial perception during minimally invasive procedures, thereby enhancing surgical precision and safety, with crucial implications for augmented reality and navigation systems.

Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy

Depth estimation is a cornerstone of 3D reconstruction and plays a vital role in minimally invasive endoscopic surgeries. However, most current depth estimation networks rely on traditional convolutional neural networks, which are limited in their ability to capture global information. Foundation models offer a promising avenue for enhancing depth estimation, but those currently available are primarily trained on natural images, leading to suboptimal performance when applied to endoscopic images. In this work, we introduce a novel fine-tuning strategy for the Depth Anything Model and integrate it with an intrinsic-based unsupervised monocular depth estimation framework. Our approach includes a low-rank adaptation technique based on random vectors, which improves the model's adaptability to different scales. Additionally, we propose a residual block built on depthwise separable convolution to compensate for the transformer's limited ability to capture high-frequency details, such as edges and textures. Our experimental results on the SCARED dataset show that our method achieves state-of-the-art performance while minimizing the number of trainable parameters. Applying this method in minimally invasive endoscopic surgery could significantly enhance both the precision and safety of these procedures.

RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection

Radiotherapy is one of the primary treatment methods for tumors, but the organ movement caused by respiratory motion limits its accuracy. Recently, 3D imaging from single X-ray projection receives extensive attentions as a promising way to address this issue. However, current methods can only reconstruct 3D image without direct location of the tumor and are only validated for fixed-angle imaging, which fails to fully meet the requirement of motion control in radiotherapy. In this study, we propose a novel imaging method RT-SRTS which integrates 3D imaging and tumor segmentation into one network based on the multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from single X-ray projection at any angle. Futhermore, we propose the attention enhanced calibrator (AEC) and uncertain-region elaboration (URE) modules to aid feature extraction and improve segmentation accuracy. We evaluated the proposed method on ten patient cases and compared it with two state-of-the-art methods. Our approach not only delivered superior 3D reconstruction but also demonstrated commendable tumor segmentation results. The simultaneous reconstruction and segmentation could be completed in approximately 70 ms, significantly faster than the required time threshold for real-time tumor tracking. The efficacy of both AEC and URE was also validated through ablation studies.