Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks

• URL: http://arxiv.org/abs/2306.16556v2
• Date: Fri, 25 Aug 2023 05:09:32 GMT
• Title: Inter-Rater Uncertainty Quantification in Medical Image Segmentation via Rater-Specific Bayesian Neural Networks
• Authors: Qingqiao Hu, Hao Wang, Jing Luo, Yunhao Luo, Zhiheng Zhangg, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze, Jianguo Zhang, Hongwei Bran Li
• Abstract summary: We introduce a novel Bayesian neural network-based architecture to estimate inter-rater uncertainty in medical image segmentation. Firstly, we introduce a one-encoder-multi-decoder architecture specifically tailored for uncertainty estimation. Secondly, we propose Bayesian modeling for the new architecture, allowing efficient capture of the inter-rater distribution.
• Score: 7.642026462053574
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automated medical image segmentation inherently involves a certain degree of uncertainty. One key factor contributing to this uncertainty is the ambiguity that can arise in determining the boundaries of a target region of interest, primarily due to variations in image appearance. On top of this, even among experts in the field, different opinions can emerge regarding the precise definition of specific anatomical structures. This work specifically addresses the modeling of segmentation uncertainty, known as inter-rater uncertainty. Its primary objective is to explore and analyze the variability in segmentation outcomes that can occur when multiple experts in medical imaging interpret and annotate the same images. We introduce a novel Bayesian neural network-based architecture to estimate inter-rater uncertainty in medical image segmentation. Our approach has three key advancements. Firstly, we introduce a one-encoder-multi-decoder architecture specifically tailored for uncertainty estimation, enabling us to capture the rater-specific representation of each expert involved. Secondly, we propose Bayesian modeling for the new architecture, allowing efficient capture of the inter-rater distribution, particularly in scenarios with limited annotations. Lastly, we enhance the rater-specific representation by integrating an attention module into each decoder. This module facilitates focused and refined segmentation results for each rater. We conduct extensive evaluations using synthetic and real-world datasets to validate our technical innovations rigorously. Our method surpasses existing baseline methods in five out of seven diverse tasks on the publicly available \emph{QUBIQ} dataset, considering two evaluation metrics encompassing different uncertainty aspects. Our codes, models, and the new dataset are available through our GitHub repository: https://github.com/HaoWang420/bOEMD-net .

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