无需标注，通过MR转CT生成合成数据实现颅骨自动分割
Unsupervised Skull Segmentation via Contrastive MR-to-CT Modality Translation
- 用对比学习实现无配对MR与CT图像的跨模态转换
- 生成高质量合成CT后在目标域完成颅骨分割，准确率高
- 适合医学影像中缺乏标注数据的颅骨分割场景
从CT扫描中进行颅骨分割已属成熟任务，但磁共振（MR）图像因存在软组织而非骨骼，该任务复杂度显著提升。在以脑部可视化为主的头颅MR图像中精准提取骨结构极具挑战性，现有基于去脑组织的方法常失效。而监督方法需耗时昂贵的颅骨标注。为此，我们提出一种完全无监督方法：不直接在MR图像上分割，而是通过MR-to-CT跨模态翻译生成合成CT数据，再在合成数据上完成分割。该方法解决无配对数据、低分辨率及泛化能力差等问题。研究对颅骨切除术、手术规划等下游任务具有重要价值，是推动医学影像中合成数据应用的关键一步。
原文摘要 · Abstract (English)
The skull segmentation from CT scans can be seen as an already solved problem. However, in MR this task has a significantly greater complexity due to the presence of soft tissues rather than bones. Capturing the bone structures from MR images of the head, where the main visualization objective is the brain, is very demanding. The attempts that make use of skull stripping seem to not be well suited for this task and fail to work in many cases. On the other hand, supervised approaches require costly and time-consuming skull annotations. To overcome the difficulties we propose a fully unsupervised approach, where we do not perform the segmentation directly on MR images, but we rather perform a synthetic CT data generation via MR-to-CT translation and perform the segmentation there. We address many issues associated with unsupervised skull segmentation including the unpaired nature of MR and CT datasets (contrastive learning), low resolution and poor quality (super-resolution), and generalization capabilities. The research has a significant value for downstream tasks requiring skull segmentation from MR volumes such as craniectomy or surgery planning and can be seen as an important step towards the utilization of synthetic data in medical imaging.
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