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Related Experiment Video

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A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

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Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach.

Suyash P Awate¹, Saurabh Garg¹, Rohit Jena¹

¹Computer Science and Engineering Department, Indian Institute of Technology (IIT) Bombay, Mumbai, India.

Medical Image Analysis

|May 16, 2019

Summary

This study introduces perfect Markov chain Monte Carlo (MCMC) sampling for accurate uncertainty estimation in medical image segmentation. The novel bounding-chain algorithm enables precise sampling from Bayesian Markov random field (MRF) models, improving clinical decision support.

Keywords:

Bayesian inference EM Hidden MRF Hippocampus Lesion Lobes MCMC MRI Multiatlas Perfect/ exact sampling Segmentation Tissue Tumor brain uncertainty

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Area of Science:

Medical Image Analysis
Computational Statistics
Machine Learning

Background:

Traditional image segmentation methods provide single optimal solutions, lacking uncertainty quantification crucial for clinical decision support.
Existing Bayesian methods for uncertainty estimation often rely on approximations, leading to deviations from true posterior distributions.
Current Markov chain Monte Carlo (MCMC) sampling techniques can be computationally intensive and may not yield accurate posterior samples.

Purpose of the Study:

To develop a novel method for accurate uncertainty estimation in medical image segmentation.
To enable sampling of multi-label segmentations from generic Bayesian Markov random field (MRF) models with exact inference.
To provide alternate close-to-optimal solutions and boundary uncertainty information for clinical decision support.

Main Methods:

Proposed the modern paradigm of perfect MCMC sampling for finite-time exact inference in generic Bayesian MRF models.
Extended Fill's algorithm to develop a novel bounding-chain algorithm for exact sampling in generic Bayesian MRFs.
Applied and evaluated the proposed methods on classic medical image analysis problems, including simulated data and clinical brain MRI.

Main Results:

The novel perfect MCMC sampling approach achieves accurate uncertainty estimates.
The bounding-chain algorithm facilitates exact sampling from generic Bayesian MRFs.
Demonstrated superior accuracy of uncertainty estimates compared to several state-of-the-art inference methods on diverse datasets.

Conclusions:

Perfect MCMC sampling offers a robust solution for uncertainty quantification in medical image segmentation.
The developed bounding-chain algorithm enhances the reliability of Bayesian inference in MRF models.
This work advances automated medical image analysis by providing more trustworthy segmentation results for clinical applications.