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Machine learning for contour classification in TG-263 noncompliant databases.

David Livermore¹, Thomas Trappenberg², Alasdair Syme^1,3

¹Department of Physics and Atmospheric Science, Dalhousie University, 6299 South St, Halifax, NS B3H 4R2, Canada.

Journal of Applied Clinical Medical Physics

|June 10, 2022

Summary

This summary is machine-generated.

This study developed an algorithm to classify head and neck cancer patient anatomy into standardized nomenclature, achieving high accuracy. The algorithm uses neural networks and contour data to improve medical data standardization.

Keywords:

image classification machine learning standardization

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Area of Science:

Medical Physics
Radiotherapy
Artificial Intelligence in Medicine

Background:

Medical data labeling often uses nonstandardized nomenclature, hindering data analysis and sharing.
The American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263) provides standards, but noncompliant databases persist.

Purpose of the Study:

To develop an algorithm for classifying anatomical contours in head and neck cancer patients according to TG-263 compliant nomenclature.
To improve the standardization of medical data for head and neck cancer treatment planning.

Main Methods:

A combined approach using binary images of anatomical contours and center of mass coordinates as input for a neural network.
Two normalization schemes for center of mass coordinates: linear (patient-agnostic) and anatomical (patient-dependent).
A voting algorithm aggregated individual slice classifications into a final classification.

Main Results:

The algorithm achieved high classification accuracy: 97.6% (non-anatomical normalization) and 97.9% (anatomical normalization) mean accuracy per class.
Overall accuracy was 99.0% with 13 errors and 98.3% with 22 errors for the non-anatomical and anatomical normalization schemes, respectively, across 1302 structures.

Conclusions:

The developed algorithm effectively classifies anatomical contours in head and neck cancer patients into TG-263 compliant nomenclature.
This approach offers a robust solution for standardizing medical data, improving consistency in radiotherapy and research.