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Towards non invasive diagnosis of scoliosis using semi-supervised learning approach

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-642-13775-4_2
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FormatText, Book Chapter
Abstract
In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.
Publication date
In
  • Image Analysis and Recognition: 10–19.
Series
  • Lecture Notes in Computer Science 6112.
LanguageEnglish
Peer reviewedYes
NRC publication
This is a non-NRC publication

"Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.

NPARC number23001008
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Record identifier4566f0ac-df71-4baf-a7e3-f1f48117a04c
Record created2016-11-28
Record modified2020-03-03
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