Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation Schmidt, Arne Morales Álvarez, Pablo Molina Soriano, Rafael Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model, called Probabilistic Inter-Observer and iNtra- Observer variation NetwOrk (Pionono). It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions. The model is optimized by variational inference and can be trained end-to-end. It outperforms state-of-the-art models such as STAPLE, Probabilistic UNet, and models based on confusion matrices. Additionally, Pionono predicts multiple coherent segmentation maps that mimic the rater’s expert opinion, which provides additional valuable information for the diagnostic process. Experiments on real-world cancer segmentation datasets demonstrate the high accuracy and efficiency of Pionono, making it a powerful tool for medical image analysis. 2024-06-10T10:19:37Z 2024-06-10T10:19:37Z 2024-01-15 conference output Published version: A. Schmidt, P. Morales-Álvarez and R. Molina, "Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation," 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023, pp. 21040-21049, doi: 10.1109/ICCV51070.2023.01929 https://hdl.handle.net/10481/92455 10.1109/ICCV51070.2023.01929 eng info:eu-repo/grantAgreement/EC/H2020/MSC 860627 http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Institute of Electrical and Electronics Engineers