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$\pm$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.

通过扩展扩散模型的领域适应能力，利用配对的 CT 和 MRI 扫描，提出了一种无需 CT 分割标准的新颖不确定性感知脑室分割技术，并展示了其对其他相似建模方法的优越性。