# Conformal Prediction for Image Segmentation Using Morphological Prediction Sets

arXiv: https://arxiv.org/abs/2503.05618 · HTML: https://mlsanity.com/paper/2503.05618/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 21 of 21)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [PraNet: Parallel Reverse Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2006.11392/) (2020)
- [Conformal Risk Control](https://mlsanity.com/paper/2208.02814/) (2022)
- [Distribution-Free, Risk-Controlling Prediction Sets](https://mlsanity.com/paper/2101.02703/) (2021)
- [A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images](https://mlsanity.com/paper/1612.00799/) (2016)
- [Conformal Prediction With Conditional Guarantees](https://mlsanity.com/paper/2305.12616/) (2023)
- [Theoretical Foundations of Conformal Prediction](https://mlsanity.com/paper/2411.11824/) (2024)
- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [Nested conformal prediction and quantile out-of-bag ensemble methods](https://mlsanity.com/paper/1910.10562/) (2019)
- [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)
- [How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control](https://mlsanity.com/paper/2302.03791/) (2023)
- [Learning the Effect of Registration Hyperparameters with HyperMorph](https://mlsanity.com/paper/2203.16680/) (2022)
- [Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook](https://mlsanity.com/paper/2312.05391/) (2023)
- [Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction](https://mlsanity.com/paper/2403.07263/) (2024)
- [Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling](https://mlsanity.com/paper/2304.06052/) (2023)
- [Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty](https://mlsanity.com/paper/2405.05145/) (2024)
- [Automatically Adaptive Conformal Risk Control](https://mlsanity.com/paper/2406.17819/) (2024)
- [Towards PAC Multi-Object Detection and Tracking](https://mlsanity.com/paper/2204.07482/) (2022)
- [Conformal Prediction for Trustworthy Detection of Railway Signals](https://mlsanity.com/paper/2301.11136/) (2023)
- [Conformal confidence sets for biomedical image segmentation](https://mlsanity.com/paper/2410.03406/) (2024)
- [Spatial-Aware Conformal Prediction for Trustworthy Hyperspectral Image Classification](https://mlsanity.com/paper/2409.01236/) (2024)

## Built on by (most cited first, 14 of 14)

- [Conditional Conformal Risk Adaptation](https://mlsanity.com/paper/2504.07611/) (2025)
- [COMPASS: Robust Feature Conformal Prediction for Medical Segmentation Metrics](https://mlsanity.com/paper/2509.22240/) (2025)
- [CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition](https://mlsanity.com/paper/2505.14113/) (2025)
- [Controlling False Positives in Image Segmentation via Conformal Prediction](https://mlsanity.com/paper/2511.15406/) (2025)
- [Conformal Prediction Sets for Instance Segmentation](https://mlsanity.com/paper/2602.10045/) (2026)
- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [Anatomically-aware conformal prediction for medical image segmentation with random walks](https://mlsanity.com/paper/2601.18997/) (2026)
- [Trustworthy Endoscopic Super-Resolution](https://mlsanity.com/paper/2604.18001/) (2026)
- [Adaptive Conformal Prediction for Reliable and Explainable Medical Image Classification](https://mlsanity.com/paper/2605.12917/) (2026)
- [When Average Calibration Fails: Site-Conditional Federated Conformal Risk Control](https://mlsanity.com/paper/2606.20115/) (2026)
- [ConRad: Efficient Conformal Prediction for Radiomics](https://mlsanity.com/paper/2607.08084/) (2026)
- [VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation](https://mlsanity.com/paper/2608.10903/) (2026)
- [Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift](https://mlsanity.com/paper/2608.18193/) (2026)
- [Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response](https://mlsanity.com/paper/2610.18789/) (2026)

## Abstract (arXiv)

Image segmentation is a challenging task influenced by multiple sources of uncertainty, such as the data labeling process or the sampling of training data. In this paper we focus on binary segmentation and address these challenges using conformal prediction, a family of model- and data-agnostic methods for uncertainty quantification that provide finite-sample theoretical guarantees and applicable to any pretrained predictor. Our approach involves computing nonconformity scores, a type of prediction residual, on held-out calibration data not used during training. We use dilation, one of the fundamental operations in mathematical morphology, to construct a margin added to the borders of predicted segmentation masks. At inference, the predicted set formed by the mask and its margin contains the ground-truth mask with high probability, at a confidence level specified by the user. The size of the margin serves as an indicator of predictive uncertainty for a given model and dataset. We work in a regime of minimal information as we do not require any feedback from the predictor: only the predicted masks are needed for computing the prediction sets. Hence, our method is applicable to any segmentation model, including those based on deep learning; we evaluate our approach on several medical imaging applications.