# Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net

arXiv: https://arxiv.org/abs/2407.21273 · HTML: https://mlsanity.com/paper/2407.21273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding](https://mlsanity.com/paper/1511.02680/) (2015)
- [Better Uncertainty Calibration via Proper Scores for Classification and Beyond](https://mlsanity.com/paper/2203.07835/) (2022)
- [How to Use Dropout Correctly on Residual Networks with Batch Normalization](https://mlsanity.com/paper/2302.06112/) (2023)
- [An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions](https://mlsanity.com/paper/2110.03260/) (2021)
- [MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts](https://mlsanity.com/paper/2007.03995/) (2020)
- [Reslicing Ultrasound Images for Data Augmentation and Vessel Reconstruction](https://mlsanity.com/paper/2301.07286/) (2023)

## Built on by (most cited first, 1 of 1)

- [Bifurcation Identification for Ultrasound-driven Robotic Cannulation](https://mlsanity.com/paper/2409.06817/) (2024)

## Abstract (arXiv)

Efficient intravascular access in trauma and critical care significantly impacts patient outcomes. However, the availability of skilled medical personnel in austere environments is often limited. Autonomous robotic ultrasound systems can aid in needle insertion for medication delivery and support non-experts in such tasks. Despite advances in autonomous needle insertion, inaccuracies in vessel segmentation predictions pose risks. Understanding the uncertainty of predictive models in ultrasound imaging is crucial for assessing their reliability. We introduce MSU-Net, a novel multistage approach for training an ensemble of U-Nets to yield accurate ultrasound image segmentation maps. We demonstrate substantial improvements, 18.1% over a single Monte Carlo U-Net, enhancing uncertainty evaluations, model transparency, and trustworthiness. By highlighting areas of model certainty, MSU-Net can guide safe needle insertions, empowering non-experts to accomplish such tasks.