# CT-based brain ventricle segmentation via diffusion Schr"odinger Bridge without target domain ground truths

arXiv: https://arxiv.org/abs/2405.18267 · HTML: https://mlsanity.com/paper/2405.18267/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 28 May 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Deep Evidential Regression](https://mlsanity.com/paper/1910.02600/) (2019)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Unsupervised Medical Image Translation with Adversarial Diffusion Models](https://mlsanity.com/paper/2207.08208/) (2022)
- [Unpaired Image-to-Image Translation via Neural Schr"odinger Bridge](https://mlsanity.com/paper/2305.15086/) (2023)
- [SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth](https://mlsanity.com/paper/1810.06498/) (2018)
- [Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation](https://mlsanity.com/paper/2308.09345/) (2023)
- [Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets](https://mlsanity.com/paper/1901.11369/) (2019)
- [Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge](https://mlsanity.com/paper/2110.01607/) (2021)
- [Domain Adaptation using Silver Standard Masks for Lateral Ventricle Segmentation in FLAIR MRI](https://mlsanity.com/paper/2307.08456/) (2023)

## Built on by (most cited first, 2 of 2)

- [FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation](https://mlsanity.com/paper/2512.05140/) (2025)
- [Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation](https://mlsanity.com/paper/2412.19026/) (2024)

## Abstract (arXiv)

Efficient and accurate brain ventricle segmentation from clinical CT scans is critical for emergency surgeries like ventriculostomy. With the challenges in poor soft tissue contrast and a scarcity of well-annotated databases for clinical brain CTs, we introduce a novel uncertainty-aware ventricle segmentation technique without the need of CT segmentation ground truths by leveraging diffusion-model-based domain adaptation. Specifically, our method employs the diffusion Schr"odinger Bridge and an attention recurrent residual U-Net to capitalize on unpaired CT and MRI scans to derive automatic CT segmentation from those of the MRIs, which are more accessible. Importantly, we propose an end-to-end, joint training framework of image translation and segmentation tasks, and demonstrate its benefit over training individual tasks separately. By comparing the proposed method against similar setups using two different GAN models for domain adaptation (CycleGAN and CUT), we also reveal the advantage of diffusion models towards improved segmentation and image translation quality. With a Dice score of 0.78±0.27, our proposed method outperformed the compared methods, including SynSeg-Net, while providing intuitive uncertainty measures to further facilitate quality control of the automatic segmentation outcomes. The implementation of our proposed method is available at: https://github.com/HealthX-Lab/DiffusionSynCTSeg.