# MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy

Source: [MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy](<https://www.sota2.com/research/paper/68fa74c61e209131cea63b94>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T07:13:00.848Z

Authors: John J. Han, Ayberk Acar, Nicholas Kavoussi, Jie Ying Wu

Published: 2024-04-03T18:40:48.000Z

## Abstract

Style transfer is a promising approach to close the sim-to-real gap in medical endoscopy. Rendering synthetic endoscopic videos by traversing pre-operative scans (such as MRI or CT) can generate structurally accurate simulations as well as ground truth camera poses and depth maps. Although image-to-image (I2I) translation models such as CycleGAN can imitate realistic endoscopic images from these simulations, they are unsuitable for video-to-video synthesis due to the lack of temporal consistency, resulting in artifacts between frames. We propose MeshBrush, a neural mesh stylization method to synthesize temporally consistent videos with differentiable rendering. MeshBrush uses the underlying geometry of patient imaging data while leveraging existing I2I methods. With learned per-vertex textures, the stylized mesh guarantees consistency while producing high-fidelity outputs. We demonstrate that mesh stylization is a promising approach for creating realistic simulations for downstream tasks such as training networks and preoperative planning. Although our method is tested and designed for ureteroscopy, its components are transferable to general endoscopic and laparoscopic procedures. The code will be made public on GitHub.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Feature Matching on In-house real endoscopy image dataset](<https://www.sota2.com/research/sota/feature-matching-on-in-house-real-endoscopy-image-dataset.md>): Feature Matching; In-house real endoscopy image dataset; ORB-1 Accuracy: 92.1.

- [Realism Assessment on In-house real endoscopy image dataset](<https://www.sota2.com/research/sota/realism-assessment-on-in-house-real-endoscopy-image-dataset.md>): Realism Assessment; In-house real endoscopy image dataset; FID: 187.1.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2404.02999>)

[PDF](<https://arxiv.org/pdf/2404.02999.pdf>)

[Implementation](<https://www.slicer.org/>)