# Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation

arXiv: https://arxiv.org/abs/1806.03106 · HTML: https://mlsanity.com/paper/1806.03106/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis](https://mlsanity.com/paper/2210.03736/) (2022)
- [A Review of Uncertainty Estimation and its Application in Medical Imaging](https://mlsanity.com/paper/2302.08119/) (2023)
- [Transformer-based out-of-distribution detection for clinically safe segmentation](https://mlsanity.com/paper/2205.10650/) (2022)
- [FSNet: A Failure Detection Framework for Semantic Segmentation](https://mlsanity.com/paper/2108.08748/) (2021)
- [U-LanD: Uncertainty-Driven Video Landmark Detection](https://mlsanity.com/paper/2102.01586/) (2021)
- [A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation](https://mlsanity.com/paper/2411.16370/) (2024)
- [Using uncertainty estimation to reduce false positives in liver lesion detection](https://mlsanity.com/paper/2101.04386/) (2021)
- [Hardware-aware Real-time Myocardial Segmentation Quality Control in Contrast Echocardiography](https://mlsanity.com/paper/2109.06909/) (2021)
- [Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows](https://mlsanity.com/paper/2112.03277/) (2021)
- [Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery](https://mlsanity.com/paper/2304.05040/) (2023)
- [AI-Assisted Decision-Making for Clinical Assessment of Auto-Segmented Contour Quality](https://mlsanity.com/paper/2505.00308/) (2025)

## Abstract (arXiv)

Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model. Reliable uncertainties are particularly interesting for safety-critical computer-assisted applications in medicine, e.g., neurosurgical interventions and radiotherapy planning. We propose an uncertainty-driven sanity check for the identification of segmentation results that need particular expert review. Our method uses a fully-convolutional neural network and computes uncertainty estimates by the principle of Monte Carlo dropout. We evaluate the performance of the proposed method on a clinical dataset with 30 postoperative brain tumor images. The method can segment the highly inhomogeneous resection cavities accurately (Dice coefficients 0.792 ± 0.154). Furthermore, the proposed sanity check is able to detect the worst segmentation and three out of the four outliers. The results highlight the potential of using the additional information from the model's parameter uncertainty to validate the segmentation performance of a deep learning model.