# BronchoGAN: Anatomically consistent and domain-agnostic image-to-image translation for video bronchoscopy

arXiv: https://arxiv.org/abs/2507.01387 · HTML: https://mlsanity.com/paper/2507.01387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Surgical-DINO: Adapter Learning of Foundation Models for Depth Estimation in Endoscopic Surgery](https://mlsanity.com/paper/2401.06013/) (2024)
- [Self-Consistent Recursive Diffusion Bridge for Medical Image Translation](https://mlsanity.com/paper/2405.06789/) (2024)
- [I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling](https://mlsanity.com/paper/2405.14022/) (2024)
- [Generative Adversarial Network for Medical Images (MI-GAN)](https://mlsanity.com/paper/1810.00551/) (2018)
- [Autonomous Driving in the Lung using Deep Learning for Localization](https://mlsanity.com/paper/1907.08136/) (2019)
- [Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy](https://mlsanity.com/paper/2208.11468/) (2022)
- [Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge](https://mlsanity.com/paper/2307.08318/) (2023)
- [Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization](https://mlsanity.com/paper/2205.10368/) (2022)
- [Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data](https://mlsanity.com/paper/2402.01186/) (2024)

## Built on by (most cited first, 1 of 1)

- [BronchoLumen: Analysis of recent YOLO-based architectures for real-time bronchial orifice detection in video bronchoscopy](https://mlsanity.com/paper/2605.11748/) (2026)

## Abstract (arXiv)

The limited availability of bronchoscopy images makes image synthesis particularly interesting for training deep learning models. Robust image translation across different domains -- virtual bronchoscopy, phantom as well as in-vivo and ex-vivo image data -- is pivotal for clinical applications. This paper proposes BronchoGAN introducing anatomical constraints for image-to-image translation being integrated into a conditional GAN. In particular, we force bronchial orifices to match across input and output images. We further propose to use foundation model-generated depth images as intermediate representation ensuring robustness across a variety of input domains establishing models with substantially less reliance on individual training datasets. Moreover our intermediate depth image representation allows to easily construct paired image data for training. Our experiments showed that input images from different domains (e.g. virtual bronchoscopy, phantoms) can be successfully translated to images mimicking realistic human airway appearance. We demonstrated that anatomical settings (i.e. bronchial orifices) can be robustly preserved with our approach which is shown qualitatively and quantitatively by means of improved FID, SSIM and dice coefficients scores. Our anatomical constraints enabled an improvement in the Dice coefficient of up to 0.43 for synthetic images. Through foundation models for intermediate depth representations, bronchial orifice segmentation integrated as anatomical constraints into conditional GANs we are able to robustly translate images from different bronchoscopy input domains. BronchoGAN allows to incorporate public CT scan data (virtual bronchoscopy) in order to generate large-scale bronchoscopy image datasets with realistic appearance. BronchoGAN enables to bridge the gap of missing public bronchoscopy images.