# Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels

arXiv: https://arxiv.org/abs/2108.04114 · HTML: https://mlsanity.com/paper/2108.04114/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations](https://mlsanity.com/paper/1707.03237/) (2017)
- [Let's agree to disagree: learning highly debatable multirater labelling](https://mlsanity.com/paper/1909.01891/) (2019)
- [Detection of False Positive and False Negative Samples in Semantic Segmentation](https://mlsanity.com/paper/1912.03673/) (2019)
- [Prostate Segmentation from Ultrasound Images using Residual Fully Convolutional Network](https://mlsanity.com/paper/1903.08814/) (2019)

## Built on by (most cited first, 5 of 5)

- [Segmentation by registration-enabled SAM prompt engineering using five reference images](https://mlsanity.com/paper/2407.17933/) (2024)
- [Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images](https://mlsanity.com/paper/2308.11376/) (2023)
- [Promptable cancer segmentation using minimal expert-curated data](https://mlsanity.com/paper/2505.17915/) (2025)
- [SPARS: Self-Play Adversarial Reinforcement Learning for Segmentation of Liver Tumours](https://mlsanity.com/paper/2505.18989/) (2025)
- [Active few-shot segmentation by reinforcing data selection](https://mlsanity.com/paper/2607.22371/) (2026)

## Abstract (arXiv)

When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accuracy on the classifier-selected frames, that would be segmented, remains comparable to or better than those from standalone segmentation networks. Interestingly, the efficacy of the pre-screening classifier was affected by the sampling methods for training labels from multiple observers, a seemingly independent problem. We show experimentally that a previously proposed approach, combining random sampling and consensus labels, may need to be adapted to perform well in our application. Furthermore, this work aims to share practical experience in developing a machine learning application that assists highly variable interventional imaging for prostate cancer patients, to present robust and reproducible open-source implementations, and to report a set of comprehensive results and analysis comparing these practical, yet important, options in a real-world clinical application.