用合成数据融合术前CT与术中CBCT，提升分割精度
Initial Study On Improving Segmentation By Combining Preoperative CT And Intraoperative CBCT Using Synthetic Data
- 通过多模态学习融合粗对齐的CT与CBCT扫描
- 20组实验中18组分割性能得到提升
- 适合需要高精度医学图像分割的临床研究者
计算机辅助干预可实现精准、微创手术，常依赖先进成像技术。锥形束计算机断层扫描（CBCT）虽可用于辅助干预，但常受伪影影响，难以准确解读。尽管图像质量下降会影响分析，但术前高质量扫描仍具改进潜力。本文研究在术前CT与术中CBCT均可用，但扫描间配准不精确（模拟真实场景）的情况下，如何利用两者信息提升分割性能。提出一种多模态学习方法，融合粗对齐的CBCT与CT扫描，并在包含真实CT和合成CBCT体数据及对应体素标注的合成数据上进行验证。结果显示，在20个测试设置中，有18组分割性能获得提升。
原文摘要 · Abstract (English)
Computer-Assisted Interventions enable clinicians to perform precise, minimally invasive procedures, often relying on advanced imaging methods. Cone-beam computed tomography (CBCT) can be used to facilitate computer-assisted interventions, despite often suffering from artifacts that pose challenges for accurate interpretation. While the degraded image quality can affect image analysis, the availability of high quality, preoperative scans offers potential for improvements. Here we consider a setting where preoperative CT and intraoperative CBCT scans are available, however, the alignment (registration) between the scans is imperfect to simulate a real world scenario. We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans and investigate the effect on segmentation performance. For this experiment we use synthetically generated data containing real CT and synthetic CBCT volumes with corresponding voxel annotations. We show that this fusion setup improves segmentation performance in $18$ out of $20$ investigated setups.
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