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TED: A Tolerant Edit Distance for segmentation evaluation.

Jan Funke¹, Jonas Klein², Francesc Moreno-Noguer³

¹Institut de Robòtica i Informàtica Industrial, UPC/CSIC, Barcelona, Spain; Janelia Research Campus, VA, Ashburn, United States; Institute of Neuroinformatics, UZH/ETH, Zurich, Switzerland.

Methods (San Diego, Calif.)

|January 22, 2017

Summary

This summary is machine-generated.

We introduce Tolerant Edit Distance (TED), a novel error measure for image segmentation. TED quantifies segmentation errors, considering tolerable inaccuracies and manual correction effort, improving accuracy in biomedical applications.

Keywords:

Computer vision Electron microscopy Evaluation Learning Neuron segmentation Segmentation

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Area of Science:

Biomedical image processing
Computer vision
Machine learning

Background:

Evaluating image segmentation accuracy is crucial in biomedical applications.
Existing error measures often lack the flexibility to account for tolerable inaccuracies.
Manual correction of segmentation errors is labor-intensive and requires accurate error quantification.

Purpose of the Study:

To develop a novel error measure, Tolerant Edit Distance (TED), for comparing computer-generated segmentations against ground truth.
To create a measure that accounts for application-specific tolerable errors and the effort required for manual correction.
To demonstrate the utility of TED in evaluating and improving segmentation algorithms.

Main Methods:

The Tolerant Edit Distance (TED) is defined as the minimal weighted sum of split and merge operations to transform one segmentation into another within specified tolerance bounds.
TED was applied to 3D segmentations of neurons in electron microscopy images.
TED was integrated as a loss function within a max-margin learning framework for automatic neuron segmentation parameter optimization.

Main Results:

TED provides intuitive error quantification, enabling localization and classification of errors.
The measure effectively evaluates 3D neuron segmentations where topological correctness is prioritized over exact boundary locations.
Training segmentation algorithms using TED as a loss function resulted in higher accuracy compared to other learning methods.

Conclusions:

Tolerant Edit Distance (TED) offers a flexible and intuitive approach to evaluating image segmentation accuracy in biomedical contexts.
TED's ability to incorporate application-specific tolerances and reflect manual correction effort enhances its practical applicability.
Utilizing TED in machine learning frameworks can significantly improve the performance of automatic segmentation algorithms, particularly for complex structures like neurons.