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Eigen-disfigurement model for simulating plausible facial disfigurement after reconstructive surgery

Overview of attention for article published in BMC Medical Imaging, March 2015
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Title
Eigen-disfigurement model for simulating plausible facial disfigurement after reconstructive surgery
Published in
BMC Medical Imaging, March 2015
DOI 10.1186/s12880-015-0050-7
Pubmed ID
Authors

Juhun Lee, Michelle C Fingeret, Alan C Bovik, Gregory P Reece, Roman J Skoracki, Matthew M Hanasono, Mia K Markey

Abstract

Patients with facial cancers can experience disfigurement as they may undergo considerable appearance changes from their illness and its treatment. Individuals with difficulties adjusting to facial cancer are concerned about how others perceive and evaluate their appearance. Therefore, it is important to understand how humans perceive disfigured faces. We describe a new strategy that allows simulation of surgically plausible facial disfigurement on a novel face for elucidating the human perception on facial disfigurement. Longitudinal 3D facial images of patients (N = 17) with facial disfigurement due to cancer treatment were replicated using a facial mannequin model, by applying Thin-Plate Spline (TPS) warping and linear interpolation on the facial mannequin model in polar coordinates. Principal Component Analysis (PCA) was used to capture longitudinal structural and textural variations found within each patient with facial disfigurement arising from the treatment. We treated such variations as disfigurement. Each disfigurement was smoothly stitched on a healthy face by seeking a Poisson solution to guided interpolation using the gradient of the learned disfigurement as the guidance field vector. The modeling technique was quantitatively evaluated. In addition, panel ratings of experienced medical professionals on the plausibility of simulation were used to evaluate the proposed disfigurement model. The algorithm reproduced the given face effectively using a facial mannequin model with less than 4.4 mm maximum error for the validation fiducial points that were not used for the processing. Panel ratings of experienced medical professionals on the plausibility of simulation showed that the disfigurement model (especially for peripheral disfigurement) yielded predictions comparable to the real disfigurements. The modeling technique of this study is able to capture facial disfigurements and its simulation represents plausible outcomes of reconstructive surgery for facial cancers. Thus, our technique can be used to study human perception on facial disfigurement.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 11%
Student > Doctoral Student 3 11%
Researcher 3 11%
Student > Postgraduate 3 11%
Student > Master 3 11%
Other 5 19%
Unknown 7 26%
Readers by discipline Count As %
Medicine and Dentistry 15 56%
Psychology 3 11%
Chemistry 1 4%
Biochemistry, Genetics and Molecular Biology 1 4%
Unknown 7 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2015.
All research outputs
#15,327,280
of 22,796,179 outputs
Outputs from BMC Medical Imaging
#265
of 596 outputs
Outputs of similar age
#157,141
of 263,558 outputs
Outputs of similar age from BMC Medical Imaging
#9
of 12 outputs
Altmetric has tracked 22,796,179 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 596 research outputs from this source. They receive a mean Attention Score of 2.1. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,558 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.