@misc{10481/92455,
year = {2024},
month = {1},
url = {https://hdl.handle.net/10481/92455},
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 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.},
organization = {European Union’s H2020
research and innovation programme (Marie Skłodowska
Curie grant agreement No 860627, CLARIFY Project)},
organization = {Spanish Ministry of Science and Innovation (project
PID2019-105142RB-C22)},
organization = {FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria,
Conocimiento y Universidades (project P20 00286)},
publisher = {Institute of Electrical and Electronics Engineers},
title = {Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation},
doi = {10.1109/ICCV51070.2023.01929},
author = {Schmidt, Arne and Morales Álvarez, Pablo and Molina Soriano, Rafael},
}