# Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction

arXiv: https://arxiv.org/abs/2301.07286 · HTML: https://mlsanity.com/paper/2301.07286/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)

## Built on by (most cited first, 4 of 4)

- [Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net](https://mlsanity.com/paper/2407.21273/) (2024)
- [Bifurcation Identification for Ultrasound-driven Robotic Cannulation](https://mlsanity.com/paper/2409.06817/) (2024)
- [Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation](https://mlsanity.com/paper/2306.13329/) (2023)
- [Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms](https://mlsanity.com/paper/2503.06795/) (2025)

## Abstract (arXiv)

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.