# Exploring Structure-Wise Uncertainty for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2211.00303 · HTML: https://mlsanity.com/paper/2211.00303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 1 Nov 2022.

## Builds on (most cited first, 16 of 16)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Understanding Measures of Uncertainty for Adversarial Example Detection](https://mlsanity.com/paper/1803.08533/) (2018)
- [Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation](https://mlsanity.com/paper/1911.13273/) (2019)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation](https://mlsanity.com/paper/1907.03338/) (2019)
- [Leveraging Uncertainty Estimates for Predicting Segmentation Quality](https://mlsanity.com/paper/1807.00502/) (2018)
- [Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT](https://mlsanity.com/paper/1905.12806/) (2019)
- [Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control](https://mlsanity.com/paper/1811.09800/) (2018)
- [Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal Modeling](https://mlsanity.com/paper/1907.08915/) (2019)
- [Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection](https://mlsanity.com/paper/1712.00497/) (2017)
- [Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning](https://mlsanity.com/paper/2108.11693/) (2021)
- [Using uncertainty estimation to reduce false positives in liver lesion detection](https://mlsanity.com/paper/2101.04386/) (2021)
- [Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting](https://mlsanity.com/paper/1911.05530/) (2019)
- [Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation](https://mlsanity.com/paper/2107.08543/) (2021)

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

- [Confidence Contours: Uncertainty-Aware Annotation for Medical Semantic Segmentation](https://mlsanity.com/paper/2308.07528/) (2023)

## Abstract (arXiv)

When applying a Deep Learning model to medical images, it is crucial to estimate the model uncertainty. Voxel-wise uncertainty is a useful visual marker for human experts and could be used to improve the model's voxel-wise output, such as segmentation. Moreover, uncertainty provides a solid foundation for out-of-distribution (OOD) detection, improving the model performance on the image-wise level. However, one of the frequent tasks in medical imaging is the segmentation of distinct, local structures such as tumors or lesions. Here, the structure-wise uncertainty allows more precise operations than image-wise and more semantic-aware than voxel-wise. The way to produce uncertainty for individual structures remains poorly explored. We propose a framework to measure the structure-wise uncertainty and evaluate the impact of OOD data on the model performance. Thus, we identify the best UE method to improve the segmentation quality. The proposed framework is tested on three datasets with the tumor segmentation task: LIDC-IDRI, LiTS, and a private one with multiple brain metastases cases.