Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Wallner, J; Hochegger, K; Chen, X; Mischak, I; Reinbacher, K; Pau, M; Zrnc, T; Schwenzer-Zimmerer, K; Zemann, W; Schmalstieg, D; Egger, J.
Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action.
PLoS One. 2018; 13(5):e0196378-e0196378 [OPEN ACCESS]
Web of Science PubMed PUBMED Central FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Pau Mauro
Wallner Jürgen
Zemann Wolfgang
Zrnc Tomislav
Altmetrics:

Dimensions Citations:

Plum Analytics:
Number of Figures: 11
| | | | | | | | | | |
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 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 and the Hausdorff distance. Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups. 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. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

© Meduni Graz Impressum