Robot-guided catheter insertion has the potential to deliver urgent medical care in situations where medical personnel are unavailable. However, this technique requires accurate and reliable segmentation of anatomical landmarks in the body. For the ultrasound imaging modality, obtaining large amounts of training data for a segmentation model is time-consuming and expensive. This paper introduces RESUS (RESlicing of UltraSound Images), a weak supervision data augmentation technique for ultrasound images based on slicing reconstructed 3D volumes from tracked 2D images. This technique allows us to generate views which cannot be easily obtained in vivo due to physical constraints of ultrasound imaging, and use these augmented ultrasound images to train a semantic segmentation model. We demonstrate that RESUS achieves statistically significant improvement over training with non-augmented images and highlight qualitative improvements through vessel reconstruction.
翻译:机器人制导导导管插入有可能在没有医务人员的情况下提供紧急医疗护理。 但是,这一技术需要准确和可靠地分解身体上的解剖标志。 对于超声波成像模式,为分解模型获得大量培训数据既费时又费钱。本文介绍了基于从跟踪的2D图象中切割的三维体积的超声波图像的微弱监督数据增强技术,即超声波成像技术。这一技术使我们能够生成由于超声波成像的物理限制而无法轻易获得的视觉,并利用这些增强的超声波图象来培训语义分解模型。我们证明,RESUS在统计学上取得了显著的改进,而不是通过非振荡图像的培训,我们通过船舶重建突出质量的改进。