Trial document



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  DRKS00014337

Trial Description

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Title

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone

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Trial Acronym

enFaced

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URL of the Trial

https://www.tugraz.at/index.php?id=26448

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Brief Summary in Lay 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 a commonly available and license-free segmentation method in the mandible. The assessment is done in a comparison between segmented CT-image data of the mandible. Segmented patient-specific clinical image data performed by an automatic segmentation algorithm 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 a license-free, open-source segmentation approach in the mandible 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 a commonly available and license-free segmentation method in the mandible. The assessment is done in a comparison between segmented CT-image data of the mandible. Segmented patient-specific clinical image data performed by an automatic segmentation algorithm 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 a license-free, open-source segmentation approach in the mandible 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

  •   DRKS00014337
  •   2018/05/04
  •   [---]*
  •   yes
  •   Approved
  •   EK-29-143 ex 16/17, Ethikkommission der Medizinischen Universität Graz
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Secondary IDs

  • [---]*
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Health Condition or Problem studied

  •   bone fracture, bone defect
  •   S02 -  Fracture of skull and facial bones
  •   K10 -  Other diseases of jaws
  •   K07 -  Dentofacial anomalies [including malocclusion]
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Interventions/Observational Groups

  •   Establishment of a CT database of the mandible with datasets from the clinical routine for the evaluation of an image-based computer program.
  •   Automatic lower jawbone segmentation with an open-source algorithm on CT data basis: 1 time of measurement (T) with Hausdorff Distance (HD, voxel)/ Dice Similarity Score (DSC, %)/ Volume (V, mm3)/ Voxel (Vx, Number)/ Time (t, min., sec.)
  •   Manual lower jawbone segmentation by clinical experts (Ground Truth) on CT data basis: 1 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
  •   Observational study
  •   Non-randomized controlled trial
  •   Open (masking not used)
  •   [---]*
  •   Other
  •   Basic research/physiological study
  •   Parallel
  •   N/A
  •   N/A
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Primary Outcome

Comparison image based segmentation of lower jaw 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 lower jaw 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

  • Medical Center 
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Recruitment

  •   Planned
  •   2018/07/01
  •   10
  •   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 lower jawbones
• Age ≥18 Years
• Age <99 Years
• Data due to the medical indication in the clinical diagnostic at the department of Oral and maxillofacial surgery

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Exclusion Criteria

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

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Addresses

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    • Medizinische Universität Graz
    • Mr.  DDDr.  Jürgen  Wallner 
    • Auenbrugger Platz 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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    • Fonds zur Förderung der wissenschaftlichen Forschung (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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    •   [---]*
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Status

  •   Recruiting planned
  •   [---]*
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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.