EndoPerfect: A Hybrid NeRF-Stereo Vision Approach Pioneering Monocular Depth Estimation and 3D Reconstruction in Endoscopy

• URL: http://arxiv.org/abs/2410.04041v4
• Date: Thu, 09 Jan 2025 00:39:56 GMT
• Title: EndoPerfect: A Hybrid NeRF-Stereo Vision Approach Pioneering Monocular Depth Estimation and 3D Reconstruction in Endoscopy
• Authors: Pengcheng Chen, Wenhao Li, Nicole Gunderson, Jeremy Ruthberg, Randall Bly, Zhenglong Sun, Waleed M. Abuzeid, Eric J. Seibel,
• Abstract summary: 3D reconstruction in endoscopic sinus surgery (ESS) demands exceptional accuracy. This poses a formidable challenge when using conventional monocular endoscopes. We present a novel, highly generalizable method that combines Neural Radiance Fields (NeRF) and stereo depth estimation for 3D reconstruction.
• Score: 11.798218793025974
• License:
• Abstract: 3D reconstruction in endoscopic sinus surgery (ESS) demands exceptional accuracy, with the mean error and standard deviation necessitating within the range of a single CT slice (0.625 mm), as the critical structures in the nasal cavity are situated within submillimeter distances from surgical instruments. This poses a formidable challenge when using conventional monocular endoscopes. Depth estimation is crucial for 3D reconstruction, yet existing depth estimation methodologies either suffer from inherent accuracy limitations or, in the case of learning-based approaches, perform poorly when applied to ESS despite succeeding on their original datasets. In this study, we present a novel, highly generalizable method that combines Neural Radiance Fields (NeRF) and stereo depth estimation for 3D reconstruction that can derive metric monocular depth. Our approach begins with an initial NeRF reconstruction yielding a coarse 3D scene, the subsequent creation of binocular pairs within coarse 3D scene, and generation of depth maps through stereo vision, These depth maps are used to supervise subsequent NeRF iteration, progressively refining NeRF and binocular depth, the refinement process continues until the depth maps converged. This recursive process generates high-accuracy depth maps from monocular endoscopic video. Evaluation in synthetic endoscopy shows a depth accuracy of 0.125 $\pm$ 0.443 mm, well within the 0.625 mm threshold. Further clinical experiments with real endoscopic data demonstrate a mean distance to CT mesh of 0.269 mm, representing the highest accuracy among monocular 3D reconstruction methods in ESS.

Related papers

• Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy [3.1186464715409983]
  We introduce a novel fine-tuning strategy for the Depth Anything Model. We integrate it with an intrinsic-based unsupervised monocular depth estimation framework. Our results on the SCARED dataset show that our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-09-12T03:04:43Z)
• Enhanced Scale-aware Depth Estimation for Monocular Endoscopic Scenes with Geometric Modeling [42.70053750500301]
  We propose a novel scale-aware framework that only uses monocular images with geometric modeling for depth estimation. Specifically, we first propose a multi-resolution depth fusion strategy to enhance the quality of monocular depth estimation. By coupling scale factors and relative depth estimation, the scale-aware depth of the monocular endoscopic scenes can be estimated.
  arXiv  Detail & Related papers  (2024-08-14T03:18:04Z)
• ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation [67.22294293695255]
  We propose a novel reconstruction pipeline with a bi-directional adaptation architecture named ToDER to get precise depth estimations. Experimental results demonstrate that our approach can precisely predict depth maps in both realistic and synthetic colonoscopy videos.
  arXiv  Detail & Related papers  (2024-07-23T14:24:26Z)
• High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces [18.948630080040576]
  We introduce a novel method for colon section reconstruction by leveraging NeuS applied to endoscopic images, supplemented by a single frame of depth map. Our approach demonstrates exceptional accuracy in completely rendering colon sections, even capturing unseen portions of the surface. This breakthrough opens avenues for achieving stable and consistently scaled reconstructions, promising enhanced quality in cancer screening procedures and treatment interventions.
  arXiv  Detail & Related papers  (2024-04-20T18:06:26Z)
• EyeLS: Shadow-Guided Instrument Landing System for Intraocular Target Approaching in Robotic Eye Surgery [51.05595735405451]
  Robotic ophthalmic surgery is an emerging technology to facilitate high-precision interventions such as retina penetration in subretinal injection and removal of floating tissues in retinal detachment. Current image-based methods cannot effectively estimate the needle tip's trajectory towards both retinal and floating targets. We propose to use the shadow positions of the target and the instrument tip to estimate their relative depth position. Our method succeeds target approaching on a retina model, and achieves an average depth error of 0.0127 mm and 0.3473 mm for floating and retinal targets respectively in the surgical simulator.
  arXiv  Detail & Related papers  (2023-11-15T09:11:37Z)
• A Quantitative Evaluation of Dense 3D Reconstruction of Sinus Anatomy from Monocular Endoscopic Video [8.32570164101507]
  We perform a quantitative analysis of a self-supervised approach for sinus reconstruction using endoscopic sequences and optical tracking. Our results show that the generated reconstructions are in high agreement with the anatomy, yielding an average point-to-mesh error of 0.91 mm. We identify that pose and depth estimation inaccuracies contribute equally to this error and that locally consistent sequences with shorter trajectories generate more accurate reconstructions.
  arXiv  Detail & Related papers  (2023-10-22T17:11:40Z)
• A geometry-aware deep network for depth estimation in monocular endoscopy [17.425158094539462]
  The proposed method is extensively validated across different datasets and clinical images. The generalizability of the proposed method achieves mean RMSE values of 12.604 (T1-L1), 9.930 (T2-L2), and 13.893 (colon) on the ColonDepth dataset.
  arXiv  Detail & Related papers  (2023-04-20T11:59:32Z)
• 3D Reconstruction of Curvilinear Structures with Stereo Matching DeepConvolutional Neural Networks [52.710012864395246]
  We propose a fully automated pipeline for both detection and matching of curvilinear structures in stereo pairs. We mainly focus on 3D reconstruction of dislocations from stereo pairs of TEM images.
  arXiv  Detail & Related papers  (2021-10-14T23:05:47Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• 4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes [69.62333053044712]
  3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single OCT images. We extend 3D CNNs to 4D-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.
  arXiv  Detail & Related papers  (2020-07-02T12:02:20Z)
• D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry [57.5549733585324]
  D3VO is a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a novel self-supervised monocular depth estimation network trained on stereo videos without any external supervision. We model the photometric uncertainties of pixels on the input images, which improves the depth estimation accuracy.
  arXiv  Detail & Related papers  (2020-03-02T17:47:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.