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Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures.

Xiaofeng Liu¹, Fangxu Xing¹, Thibault Marin¹

¹Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 USA.

Proceedings of Spie--The International Society for Optical Engineering

|October 28, 2022

Summary

This summary is machine-generated.

This study introduces a new variational inference framework to model segmentation map distributions in medical imaging, addressing inter-observer variability. The method effectively captures multi-reader differences in Magnetic Resonance (MR) imaging, improving segmentation accuracy.

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Area of Science:

Medical Imaging Analysis
Machine Learning for Healthcare
Computational Neuroscience

Background:

Medical imaging segmentation often suffers from ambiguity, leading to significant inter-reader variability and aleatoric uncertainty.
Quantifying this variability is crucial for establishing reliable reference standards in diagnosis and treatment planning.
Current segmentation methods typically produce a single output, failing to account for annotator disagreement.

Purpose of the Study:

To develop a novel variational inference framework for modeling the distribution of plausible segmentation maps from medical images.
To explicitly represent and account for multi-reader variability in image segmentation.
To improve the accuracy and robustness of segmentation by addressing inherent information loss and annotator disagreement.

Main Methods:

Proposed a variational inference framework utilizing a latent vector to encode multi-reader variability.
Employed a variational autoencoder (VAE) network to approximate the segmentation map distribution.
Optimized the evidence lower bound (ELBO) for efficient approximation of segmentation map distributions.

Main Results:

The proposed framework effectively models the distribution of plausible segmentation maps, capturing multi-reader variability.
Experimental results on QUBIQ brain growth MRI segmentation datasets demonstrated the approach's effectiveness.
The method successfully addressed inter-observer variability without sacrificing segmentation accuracy.

Conclusions:

The novel variational inference framework provides an effective solution for modeling segmentation uncertainty in medical imaging.
This approach enhances the reliability of segmentation by explicitly handling inter-observer variability.
The method shows promise for improving diagnostic and treatment tasks reliant on accurate medical image segmentation.