Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation

TL;DR

This paper introduces Pionono, a probabilistic model that captures inter- and intra-observer variability in medical image segmentation, producing multiple expert-like segmentation maps to improve accuracy and interpretability.

Contribution

The paper presents Pionono, a novel end-to-end trainable probabilistic network that models observer variability and outperforms existing segmentation methods.

Findings

Pionono achieves higher accuracy than state-of-the-art models.

It predicts multiple coherent segmentation maps.

The model effectively captures observer variability.

Abstract

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 U-Net, 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.