Back to Search
Start Over
Clinical evaluation of semi-automatic opensource algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action
- Source :
- PLoS ONE, Vol 13, Iss 5, p e0196378 (2018), PLoS ONE
- Publication Year :
- 2018
-
Abstract
- Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However - due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice. In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut (GC) was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score (DSC) and the Hausdorff distance (HD). Overall segmentation times were about one minute. Mean DSC values of over 85% and HD below 33.5 voxel could be achieved. Statistical differences between the assessment parameters were not significant (p 0.94). Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.<br />26 pages
- Subjects :
- FOS: Computer and information sciences
Computer science
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
lcsh:Medicine
Mandible
computer.software_genre
030218 nuclear medicine & medical imaging
Mathematical and Statistical Techniques
Open Science
0302 clinical medicine
Software
Voxel
Image Processing, Computer-Assisted
Medicine and Health Sciences
Segmentation
lcsh:Science
Ground truth
Multidisciplinary
Applied Mathematics
Simulation and Modeling
GrowCut algorithm
Software Engineering
3. Good health
Physical Sciences
Engineering and Technology
Regression Analysis
Anatomy
Algorithms
Statistics (Mathematics)
Open Source Software
Research Article
Computer and Information Sciences
Science Policy
Surgical and Invasive Medical Procedures
Research and Analysis Methods
Machine learning
Computer Software
03 medical and health sciences
Humans
Statistical Methods
Retrospective Studies
Mouth
Software Tools
business.industry
lcsh:R
Biology and Life Sciences
030206 dentistry
Visualization
Hausdorff distance
Jaw
lcsh:Q
Artificial intelligence
Tomography, X-Ray Computed
business
Head
Digestive System
computer
Mathematics
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, Vol 13, Iss 5, p e0196378 (2018), PLoS ONE
- Accession number :
- edsair.doi.dedup.....e7bd52f69ab2b3de9192f63da5af7a63