Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction
This addresses the challenge of accurate segmentation for robot-guided catheter insertion in medical settings, though it is incremental as it builds on existing weak supervision and augmentation methods.
The paper tackles the problem of limited training data for ultrasound image segmentation by introducing RESUS, a data augmentation technique that generates new views from reconstructed 3D volumes, resulting in statistically significant improvements in segmentation performance.
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.