Computerized quantificiation of facial weakness in facioscapulohumeral muscular dystrophy
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2015-08-10
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en
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Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is a rare hereditary and progressive muscular
disease. One of the first and most characteristic symptoms of FSHD is asymmetrical weakness
of the facial muscles. This weakness varies from minimal asymmetry to a complete lack of facial
expression. Due to this weakness, patients are limited in the use of their facial muscles and are
thus less able to express themselves in a social context, which can hinder social communication.
However, at this moment studies on (the progression of) facial weakness and the consequences
on communication are lacking and there is no validated outcome measure for facial weakness.
Facial weakness is difficult to objectify and even more difficult to follow up over time. To
facilitate future research, a standardized quantitative outcome measure for facial weakness in
FSHD is required.
Within this project a grading system for objectively measuring facial weakness and a diagnosis
system for predicting FSHD from facial weakness were developed. A novel dataset
was created consisting of facial video recordings of FSHD patients and healthy controls while
performing various tasks. Video frames at rest and maximal expression were identified and manually
labeled with 68 facial landmarks. Experts on facial weakness graded the video recordings
of the participants on degree of facial weakness and assessed if FSHD was present. After extracting
various types of facial features which were reported to quantify facial weakness, several
machine learning systems were trained and evaluated using a newly developed system evaluation
pipeline. Subsequently, the best systems were compared with human experts on agreement.
The results show that the developed facial weakness grading systems perform in high agreement
with the established ground truth, but that the agreement among human experts should
be improved. Furthermore, the developed systems predicting whether a participant has FSHD
perform above expert-level. It was found that combining multiple feature types gave the best
results and that combining 2D and 3D features yielded better results than only 2D or only
3D features. It was also found that subtraction features were the most unreliable, although
this is thought be related to insufficient head stabilization. Furthermore, the system evaluation
pipeline provides a useful framework to further investigate the contribution of features to grade
facial weakness and diagnose FSHD.
The work in this thesis shows that it is possible to create an objective facial weakness grading
system for FSHD patients with comparable performance to the current golden standard. Many
research projects on the effect of various treatments on facial weakness within FSHD could
benefit from an objective measure for reporting facial weakness. However, the current work
should be improved in many ways before it can serve as such an measure, for example the
number of participants should be increased, and a method for automatic landmark localization
should be incorporated. The presented work provides a promising starting point, which could
eventually lead to the development of a computerized standard for grading facial weakness
within FSHD patients.
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Faculteit der Sociale Wetenschappen