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.

翻译：从临床CT扫描中高效、准确地分割脑室对于脑室造口术等急诊手术至关重要。针对临床脑部CT软组织对比度差且缺乏高质量标注数据库的挑战，我们引入了一种新颖的不确定性感知脑室分割技术，该技术无需CT分割真值，而是利用基于扩散模型的域适应方法。具体而言，我们的方法采用扩散薛定谔桥和注意力循环残差U-Net，利用未配对的CT和MRI扫描，从更易获取的MRI分割结果中推导出自动的CT分割。重要的是，我们提出了一个端到端的图像翻译与分割任务联合训练框架，并证明了其优于单独训练各个任务。通过将所提方法与使用两种不同GAN模型（CycleGAN和CUT）进行域适应的类似设置进行比较，我们也揭示了扩散模型在提升分割和图像翻译质量方面的优势。所提方法取得了0.78±0.27的Dice分数，性能优于包括SynSeg-Net在内的对比方法，同时提供了直观的不确定性度量，以进一步促进自动分割结果的质量控制。