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Diagnostically relevant facial gestalt information from ordinary photos.

Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A, Nellåker C - Elife (2014)

Bottom Line: The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses.Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders.Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering Science, University of Oxford, Oxford, United Kingdom Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

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(A) The 36 facial feature points annotated by the automatic image analysis algorithm. Supra-orbital ridge (8 points), the eyes (mid points of the eyelids and eye canthi, 8 points), nose (nasion, tip, ala, subnasale and outer nares, 7 points), mouth (vermilion border lateral and vertical midpoints, 6 points), and the jaw (zygoma mandibular border, gonion, mental protrubance and chin midpoint, 7 points). (B) The annotation accuracies relative to the manually annotated ground truth of each of the computer vision modules. Points 1–8 refer to the supra-orbital ridge, points 30–36 refer to the jaw points. Accuracies for the points annotated by the modules FLA, improved FLA and CoE are shown for each syndrome and control groups. Accuracies are shown as the average error relative to the width of an eye.DOI:http://dx.doi.org/10.7554/eLife.02020.004
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fig1s1: (A) The 36 facial feature points annotated by the automatic image analysis algorithm. Supra-orbital ridge (8 points), the eyes (mid points of the eyelids and eye canthi, 8 points), nose (nasion, tip, ala, subnasale and outer nares, 7 points), mouth (vermilion border lateral and vertical midpoints, 6 points), and the jaw (zygoma mandibular border, gonion, mental protrubance and chin midpoint, 7 points). (B) The annotation accuracies relative to the manually annotated ground truth of each of the computer vision modules. Points 1–8 refer to the supra-orbital ridge, points 30–36 refer to the jaw points. Accuracies for the points annotated by the modules FLA, improved FLA and CoE are shown for each syndrome and control groups. Accuracies are shown as the average error relative to the width of an eye.DOI:http://dx.doi.org/10.7554/eLife.02020.004

Mentions: (A) A photo is automatically analyzed to detect faces and feature points are placed using computer vision algorithms. Facial feature annotation points delineate the supra-orbital ridge (8 points), the eyes (mid points of the eyelids and eye canthi, 8 points), nose (nasion, tip, ala, subnasale and outer nares, 7 points), mouth (vermilion border lateral and vertical midpoints, 6 points) and the jaw (zygoma mandibular border, gonion, mental protrubance and chin midpoint, 7 points). Shape and Appearance feature vectors are then extracted based on feature points and these determine the photo's location in Clinical Face Phenotype Space (further details on feature points in Figure 1—figure supplement 1). This location is then analyzed in the context of existing points in Clinical Face Phenotype Space to extract phenotype similarities and diagnosis hypotheses (further details on Clinical Face Phenotype Space with simulation examples in Figure 1—figure supplement 2). (B) Average faces of syndromes in the database constructed using AAM models (‘Materials and methods’) and number of individuals which each average face represents. See online version of this manuscript for animated morphing images that show facial features differing between controls and syndromes (Figure 2).


Diagnostically relevant facial gestalt information from ordinary photos.

Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A, Nellåker C - Elife (2014)

(A) The 36 facial feature points annotated by the automatic image analysis algorithm. Supra-orbital ridge (8 points), the eyes (mid points of the eyelids and eye canthi, 8 points), nose (nasion, tip, ala, subnasale and outer nares, 7 points), mouth (vermilion border lateral and vertical midpoints, 6 points), and the jaw (zygoma mandibular border, gonion, mental protrubance and chin midpoint, 7 points). (B) The annotation accuracies relative to the manually annotated ground truth of each of the computer vision modules. Points 1–8 refer to the supra-orbital ridge, points 30–36 refer to the jaw points. Accuracies for the points annotated by the modules FLA, improved FLA and CoE are shown for each syndrome and control groups. Accuracies are shown as the average error relative to the width of an eye.DOI:http://dx.doi.org/10.7554/eLife.02020.004
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4067075&req=5

fig1s1: (A) The 36 facial feature points annotated by the automatic image analysis algorithm. Supra-orbital ridge (8 points), the eyes (mid points of the eyelids and eye canthi, 8 points), nose (nasion, tip, ala, subnasale and outer nares, 7 points), mouth (vermilion border lateral and vertical midpoints, 6 points), and the jaw (zygoma mandibular border, gonion, mental protrubance and chin midpoint, 7 points). (B) The annotation accuracies relative to the manually annotated ground truth of each of the computer vision modules. Points 1–8 refer to the supra-orbital ridge, points 30–36 refer to the jaw points. Accuracies for the points annotated by the modules FLA, improved FLA and CoE are shown for each syndrome and control groups. Accuracies are shown as the average error relative to the width of an eye.DOI:http://dx.doi.org/10.7554/eLife.02020.004
Mentions: (A) A photo is automatically analyzed to detect faces and feature points are placed using computer vision algorithms. Facial feature annotation points delineate the supra-orbital ridge (8 points), the eyes (mid points of the eyelids and eye canthi, 8 points), nose (nasion, tip, ala, subnasale and outer nares, 7 points), mouth (vermilion border lateral and vertical midpoints, 6 points) and the jaw (zygoma mandibular border, gonion, mental protrubance and chin midpoint, 7 points). Shape and Appearance feature vectors are then extracted based on feature points and these determine the photo's location in Clinical Face Phenotype Space (further details on feature points in Figure 1—figure supplement 1). This location is then analyzed in the context of existing points in Clinical Face Phenotype Space to extract phenotype similarities and diagnosis hypotheses (further details on Clinical Face Phenotype Space with simulation examples in Figure 1—figure supplement 2). (B) Average faces of syndromes in the database constructed using AAM models (‘Materials and methods’) and number of individuals which each average face represents. See online version of this manuscript for animated morphing images that show facial features differing between controls and syndromes (Figure 2).

Bottom Line: The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses.Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders.Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering Science, University of Oxford, Oxford, United Kingdom Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

Show MeSH
Related in: MedlinePlus