Trial document



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  DRKS00014853

Trial Description

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Title

CT database for evaluation of automatic image segmentation programs in the maxillofacial area

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Trial Acronym

enFacedDB

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URL of the Trial

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Brief Summary in Lay Language

Computer- assisted technologies such as image- based segmentation play an important role in the diagnosis and treatment support in oral- and maxillofacial surgery. However, although many image- based segmentation approaches exist, their clinical in- house use is often strongly limited due to technical, human or financial resources. Especially in open- source based segmentation, systematic evaluations of segmentation approaches are lacking. Therefore, the aim of this study is to assess the real segmentation quality of commonly available and license- free segmentation programs in the maxillofacial area. The assessment is done in a comparison between segmented CT- image data of the maxillofacial area. Segmented patient- specific clinical image data performed by automatic segmentation algorithms are compared with the manual ground truth segmentations performed by clinical experts. Assessment parameters are amongst others the Dice Score Coefficient (DSC, %) and the Hausdorff Distance (HD, voxel).
This study is a systematic comparison that evaluates multiple license- free, open- source segmentation programs and software platforms in the maxillofacial area based on clinical CT- data for the improvement of segmentation algorithms and a potential clinical use in patient-individualized medicine in the field of oral and maxillofacial surgery. Further, the results presented are reproducible by others and can be used for both clinical and research purposes.

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Brief Summary in Scientific Language

Computer- assisted technologies such as image- based segmentation play an important in the diagnosis and treatment support in oral- and maxillofacial surgery. However, although many image- based segmentation approaches exist, their clinical in- house use is often strongly limited due to technical, human or financial resources. Especially in open- source based segmentation, systematic evaluations of segmentation approaches are lacking. Therefore, the aim of this study is to assess the real segmentation quality of commonly available and license- free segmentation programs in the maxillofacial area. The assessment is done in a comparison between segmented CT- image data of the maxillofacial area. Segmented patient- specific clinical image data performed by automatic segmentation algorithms are compared with the manual ground truth segmentations performed by clinical experts. Assessment parameters are amongst others the Dice Score Coefficient (DSC, %) and the Hausdorff Distance (HD, voxel).
This study is a systematic comparison that evaluates multiple license- free, open- source segmentation programs and software platforms in the maxillofacial area based on clinical CT- data for the improvement of segmentation algorithms and a potential clinical use in patient-individualized medicine in the field of oral and maxillofacial surgery. Further, the results presented are reproducible by others and can be used for both clinical and research purposes.

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Organizational Data

  •   DRKS00014853
  •   2018/06/29
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  •   yes
  •   Approved
  •   30-340 ex 17/18, Ethikkommission der Medizinischen Universität Graz
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Secondary IDs

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Health Condition or Problem studied

  •   S02 -  Fracture of skull and facial bones
  •   M85 -  Other disorders of bone density and structure
  •   Q75 -  Other congenital malformations of skull and face bones
  •   S09 -  Other and unspecified injuries of head
  •   K10 -  Other diseases of jaws
  •   K07 -  Dentofacial anomalies [including malocclusion]
  •   Z96 -  Presence of other functional implants
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Interventions/Observational Groups

  •   Establishment of a CT database of the maxillofacial area with datasets from the clinical routine for the comparative evaluation of multiple image- based computer programs and platforms.
  •   Automatic maxillofacial bone segmentation with computer programs on CT data basis: time of measurement (T) with Hausdorff Distance (HD, voxel)/ Dice Similarity Score (DSC, %)/ Volume (V, mm3)/ Voxel (Vx, Number)/Time (t, min., sec.)
  •   Manual maxillofacial bone segmentation by clinical experts (Ground Truth) on CT data basis: time of measurement (T) with Hausdorff Distance (HD, voxel)/ Dice Similarity Score (DSC, %)/ Volume (V, mm3)/ Voxel (Vx, Number)/ Time (t, min., sec.)
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Characteristics

  •   Non-interventional
  •   Other
  •   Non-randomized controlled trial
  •   Open (masking not used)
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  •   Other
  •   Basic research/physiological study
  •   Parallel
  •   N/A
  •   N/A
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Primary Outcome

Comparison image based segmentation of maxillofacial CT- data between automatic (algorithm) vs. manual (clinical experts) at measurement point T by Hausdorff Distance (HD, voxel) and Dice Similarity Score (DSC, %).

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Secondary Outcome

Comparison image based segmentation of maxillofacial CT- data between automatic (algorithm) vs. manual (clinical experts) at measurement point T by Volume (V, mm3), Voxel (Vx, Number), time (t, min., sec.).

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Countries of Recruitment

  •   Austria
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Locations of Recruitment

  • University Medical Center 
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Recruitment

  •   Actual
  •   2018/08/01
  •   100
  •   Monocenter trial
  •   National
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Inclusion Criteria

  •   Both, male and female
  •   18   Years
  •   99   Years
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Additional Inclusion Criteria

Physiologically functional, complete maxillofacial bones

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Exclusion Criteria

• Implants or osteosynthesis material
• Age <18 Years
• Bone necrosis or pathologic-cystic changes of the bones

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Addresses

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    • Medizinische Universität Graz
    • Mr.  DDDr.  Jürgen  Wallner 
    • Auenbruggerplatz 5/1
    • 8036  Graz
    • Austria
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    • TU Graz
    • Mr.  Dr. Dr. habil.  Jan  Egger 
    • Inffeldgasse 16c/2
    • 8010  Graz
    • Austria
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    • Medizinische Universität Graz
    • Mr.  DDDr.  Jürgen  Wallner 
    • Auenbruggerplatz 5/1
    • 8036  Graz
    • Austria
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    • Medizinische Universität Graz
    • Mr.  DDDr.  Jürgen  Wallner 
    • Auenbruggerplatz 5/1
    • 8036  Graz
    • Austria
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Sources of Monetary or Material Support

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    • FWF
    • Sensengasse 1
    • 1090  Wien
    • Austria
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    • Austrian Science Fund (FWF) KLI 678-B31: “enFaced: Virtual and Augmented Reality Training and Navigation Module for 3D-Printed Facial Defect Reconstructions” (PIs: Jürgen Wallner and Jan Egger)
    • 1090  Wien
    • Austria
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Status

  •   Recruiting ongoing
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Trial Publications, Results and other Documents

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* This entry means the parameter is not applicable or has not been set.