# Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

arXiv: https://arxiv.org/abs/2007.15963 · HTML: https://mlsanity.com/paper/2007.15963/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 30 of 58)

- [Breaking the Dilemma of Medical Image-to-image Translation](https://mlsanity.com/paper/2110.06465/) (2021)
- [Adaptive Early-Learning Correction for Segmentation from Noisy Annotations](https://mlsanity.com/paper/2110.03740/) (2021)
- [Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification](https://mlsanity.com/paper/2207.06104/) (2022)
- [SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels](https://mlsanity.com/paper/2308.12064/) (2023)
- [Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation](https://mlsanity.com/paper/2307.11397/) (2023)
- [Learning to Segment from Noisy Annotations: A Spatial Correction Approach](https://mlsanity.com/paper/2308.02498/) (2023)
- [Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis](https://mlsanity.com/paper/2203.10611/) (2022)
- [Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation](https://mlsanity.com/paper/2111.13410/) (2021)
- [Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization](https://mlsanity.com/paper/2306.03288/) (2023)
- [Estimating label quality and errors in semantic segmentation data via any model](https://mlsanity.com/paper/2307.05080/) (2023)
- [S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences](https://mlsanity.com/paper/2304.07515/) (2023)
- [Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds](https://mlsanity.com/paper/2306.03116/) (2023)
- [Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation](https://mlsanity.com/paper/2210.17398/) (2022)
- [Diversified and Personalized Multi-rater Medical Image Segmentation](https://mlsanity.com/paper/2403.13417/) (2024)
- [Impact of individual rater style on deep learning uncertainty in medical imaging segmentation](https://mlsanity.com/paper/2105.02197/) (2021)
- [Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels](https://mlsanity.com/paper/2206.07994/) (2022)
- [Multi-annotator Deep Learning: A Probabilistic Framework for Classification](https://mlsanity.com/paper/2304.02539/) (2023)
- [Transformer-based Annotation Bias-aware Medical Image Segmentation](https://mlsanity.com/paper/2306.01340/) (2023)
- [Evaluating AI systems under uncertain ground truth: a case study in dermatology](https://mlsanity.com/paper/2307.02191/) (2023)
- [Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View](https://mlsanity.com/paper/2206.14746/) (2022)
- [MedUHIP: Towards Human-In-the-Loop Medical Segmentation](https://mlsanity.com/paper/2408.01620/) (2024)
- [Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval](https://mlsanity.com/paper/2103.12328/) (2021)
- [Revisiting Active Learning under (Human) Label Variation](https://mlsanity.com/paper/2507.02593/) (2025)
- [TAX: Tendency-and-Assignment Explainer for Semantic Segmentation with Multi-Annotators](https://mlsanity.com/paper/2302.09561/) (2023)
- [Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks](https://mlsanity.com/paper/2306.16556/) (2023)
- [Multi-rater Prompting for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2404.07580/) (2024)
- [Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective](https://mlsanity.com/paper/2407.06902/) (2024)
- [dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans](https://mlsanity.com/paper/2407.20950/) (2024)
- [Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation](https://mlsanity.com/paper/2108.00713/) (2021)
- [Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth](https://mlsanity.com/paper/2109.03596/) (2021)

## Abstract (arXiv)

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, where both the annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates of the "true" segmentation labels under the influence of their own biases and competence levels. Treating these noisy labels blindly as the ground truth limits the performance that automatic segmentation algorithms can achieve. In this work, we present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions, using two coupled CNNs. The separation of the two is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the noisy training data. We first define a toy segmentation dataset based on MNIST and study the properties of the proposed algorithm. We then demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours); 3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large. The experiments also show strong ability to capture the complex spatial characteristics of annotators' mistakes.