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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation.

Thomas Buddenkotte¹, Lorena Escudero Sanchez², Mireia Crispin-Ortuzar³

¹Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany.

Computers in Biology and Medicine

|June 11, 2023

Summary

This study introduces a scalable framework for uncertainty quantification in medical image segmentation. It improves upon classical methods by providing more accurate probability estimates, enhancing active learning and human-machine collaboration.

Keywords:

Deep learning Segmentation Uncertainty quantification

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Published on: November 11, 2022

Area of Science:

Medical Image Analysis
Machine Learning
Computer Vision

Background:

Uncertainty quantification is crucial for reliable automated image analysis, especially with deep learning models.
Current methods for uncertainty quantification in deep learning models are often computationally expensive and do not scale well.
Existing approaches struggle to accurately approximate classification probabilities in complex, high-dimensional problems.

Purpose of the Study:

To develop a scalable and intuitive framework for uncertainty quantification in medical image segmentation.
To address the limitations of classical uncertainty quantification techniques in deep learning.
To provide uncertainty measurements that approximate classification probabilities for improved model interpretability and utility.

Main Methods:

Proposed a novel, scalable framework for uncertainty quantification in medical image segmentation.
Demonstrated the failure of classical methods (e.g., dropout, ensembling) to approximate classification probabilities.
Utilized k-fold cross-validation to eliminate the need for separate calibration datasets.

Main Results:

The proposed framework yields uncertainty measurements that approximate classification probabilities.
Classical uncertainty quantification approaches were shown to be inadequate for accurate probability approximation.
K-fold cross-validation effectively replaced the need for held-out calibration data.

Conclusions:

The developed framework offers a scalable and accurate solution for uncertainty quantification in medical image segmentation.
The method supports enhanced active learning and human-machine collaboration by generating reliable pseudo-labels.
This work advances the reliability and applicability of deep learning in medical imaging analysis.